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Robust image hashing using ring partition-PGNMF and local features
Ram Kumar Karsh1,
R. H. Laskar1 &
Bhanu Bhai Richhariya2
SpringerPlus volume 5, Article number: 1995 (2016) Cite this article
Image authentication is one of the challenging research areas in the multimedia technology due to the availability of image editing tools. Image hash may be used for image authentication which should be invariant to perceptually similar image and sensitive to content changes. The challenging issue in image hashing is to design a system which simultaneously provides rotation robustness, desirable discrimination, sensitivity and localization of forged area with minimum hash length.
In this paper, a perceptually robust image hashing technique based on global and local features has been proposed. The Global feature was extracted using ring partition and projected gradient nonnegative matrix factorization (PGNMF). The ring partitioning technique converts a square image into a secondary image that makes the system rotation invariant. The PGNMF which is usually faster than the other NMFs has been used to reduce the dimension of the secondary image to generate the shorter hash sequence. The local features extracted from the salient regions of the image help to localize the forged region in the maliciously manipulated images. The image hashing techniques that use only global features are limited in discrimination.
The experimental results reveal that the proposed image hashing method based on global and local features provides better discrimination capability. The proposed hashing method is tested on large image sets collected from the different standard database. It is observed from the experimental results that the proposed system is robust to content-preserving operations and is capable of localizing the counterfeit area.
The combination of global and local features is robust against the content-preserving operations, which has a desirable discriminative capability. The proposed system may be used in image authentication, forensic evidence, and image retrieval, etc.
The extensive use of the Internet and multimedia services has made the image and video processing to be a part of our day-to-day activities. On the other hand, the academia and industries are facing several challenges due to the malicious manipulations of the images using image editing tools. Recognizing similar versions of the image from huge multimedia records is still a challenging issue (Wu et al. 2007). Attempt to overcome these challenge has led to an emerging multimedia technology known as image hashing. It is a technique that extracts a short sequence from the image to represent its contents, which is used for various image processing applications (Ahmed et al. 2010; Hassan et al. 2012; Lu et al. 2004; Lu and Wu 2010; Lv and Wang 2009; Qin et al. 2012; Slaney and Casey 2008; Tang et al. 2008).
Hash function serves as an effective tool for message authentication in cryptographic applications. The hash function [such as a message digest-5 (MD-5) and a secure hash algorithm-1 (SHA-1)] defines a mapping from an arbitrary-length message to a short digest (i.e. hash string) (Stamp 2006). The prerequisite condition to accomplish the cryptographic authentication using a hashing is the avalanche effect, in which a single bit modification on the message may lead to the significant changes in the hash string. However, the over-sensitivity of the cryptographic hash function limits its applications in the multimedia domain. In the multimedia applications, the hash function should be robust against the content-preserving operations, such as the geometric transformation, format conversion, and an image compression, etc. In other words, the hash of original image and its perceptually similar version should be approximately same. Hash should be considerably different only when the visual content is distorted by the malevolent process such as object deletion/insertion operations etc. One more important property is the discriminative capability, i.e. images with dissimilar content should provide different hashes.
Various state-of-the-art image hashing methods have been proposed in the literature. Monga et al. (2006) proposed a two-stage method which consists of feature extraction and dimension reduction using clustering technique to design the final hash. It has become a commonly adopted approach for further developing the image hashing techniques. The state-of-the-art methods are either based on global (Lei et al. 2011; Swaminathan et al. 2006; Tang et al. 2008; Xiang et al. 2007) or local features (Ahmed et al. 2010; Fouad and Jianmin 2010; Khelifi and Jiang 2010; Lv and Wang 2012; Monga and Mihcak 2007; Tang et al. 2011). The image hashing techniques based on only the global feature generally provides shorter hash length, but fails to identify the local changes in the image. On the other hand, the image hashing techniques based on only the local features are generally sensitive to local changes in the image, but provides longer hash length. Xiang et al. (2007) developed an image hashing technique using image histogram that is invariant to geometric transformations. This method generally misclassifies the different images having similar histograms. Tang et al. (2008) proposed an image hashing algorithm using nonnegative matrix factorization (NMF). In this method, the secondary image is obtained by pseudo-randomly re-arranging the pixels of the normalized image. The secondary image is then passed through the NMF, the computed coefficients are then quantized and scrambled to generate the final hash. Swaminathan et al. (2006) developed a Fourier-Mellin transform based method which is rotation invariant and provides better security. Lei et al. (2011) proposed an image hashing technique based on discrete Fourier transform (DFT) and radon transform (RT). DFT is carried out on the invariant moments generated by the Radon transformation of the original image. The computed DFT coefficients are then quantized to form the final hash. Khelifi and Jiang (2010) calculated image hash on the basis of the virtual watermark detection, which is robust for the geometric deformations and content preserving operations. This method can detect only the changes in large areas. An NMF–NMF based technique has also been proposed to generate image hash (Monga and Mihcak 2007). First NMF provides a secondary image from pseudo-randomly selected sub-images and the second NMF is used to generate the low-rank matrix. Then the matrix entries are concatenated to form the NMF–NMF vector which is considered to be the final hash. This method fails to identify the counterfeit areas. An analysis of NMF–NMF method suggests that out of the three keys, the first one used for pseudo-randomly selecting sub-images is only important (Fouad and Jianmin 2010).
Tang et al. (2011) proposed a lexicographical-framework for image hashing that uses discrete cosine transform (DCT) and NMF. This method is robust to some content preserving operations, but fragile to large degree rotation. Ahmed et al. (2010) has developed an image hashing using discrete wavelet transform (DWT) and SHA-I. It has been used for tamper detection, but limited in brightness/contrast changes and rotation. An image hashing technique based on scale invariant feature transform (SIFT) and Harris detector has also been proposed Lv and Wang (2012). In this method, key points are extracted from the scale space image by using SIFT and from which the stable key points are retained by Harris detector. The shape-texture features around the stable key points are used to generate the final hash. It is robust to the geometric deformations and can also find the counterfeit area. This method does not possess rotation invariant property.
Lu et al. (2010) developed a concept of forensic hash for information assurance. It is based on RT and the scale space theory. For image authentication, it uses the side information. This method can provide the history of image manipulations and parameters for geometric deformation. It can also identify the counterfeit area. An image hashing method with the combination of global and local features has been proposed in the literature (Zhao et al. 2013). These features are based on Zernike moments and shape-texture respectively. It is robust to the most content preserving operations and can also locate the forged areas. But it is not robust to the large degree of rotation.
Tang et al. (2016a, b) proposed an image hashing based on ring partition and invariant vector distance by incorporating invariant vector distance in place of NMF. This method is robust against perceptual digital image manipulation, but it cannot localize the forged region. The entropy based method (Tang et al. 2013) creates a rotation invariant secondary image based on the ring partition and extracts entropy from each ring to generate the image hash. This method is robust to digital image manipulations and possesses good discrimination capability for the natural image, but limited discrimination capability for textural images.
Sun and Zeng (2014) designed an image hashing based on combining the Fourier-Mellin transform (FMT) and the compressive sensing (CS). This method is resilient to scaling, and transition attacks, but sensitive to large angle rotation. The global features extracted from partitioned image blocks and local feature extracted from key points of the image have been concatenated to form an image hash (Wang et al. 2015). It is robust to perceptual image manipulation, can locate the forged area but sensitive to large angle rotation. An image hashing method based on the quaternion discrete Fourier transform followed by log-polar transform has been developed (Ouyang et al. 2015). In this method, the three colors have been used to generate hash without increasing the hash length. This method is incapable of locating the counterfeit areas in the forged image. Yan et al. (2016) proposed an image hashing method using adaptive local features. It is robust against the content-preserving operations, but unable to detect color forgery. Qin et al. (2016) has designed a hashing method based on the block truncation coding (BTC). The major drawback of this method is sensitive towards brightness changes. An image hashing technique based on ring partition and color vector angle has been proposed by Tang et al. (2016a, b). This method is robust to digital image manipulation, but around 20% of the corner information has not been utilized to design the image hash.
Image rotation is a normal content preserving operation that is considered to be a major challenging issue in the case of image hashing. Most of the conventional image hashing methods categorize the rotated images as different images (Fridrich and Goljan 2000; Qin et al. 2012; Lin and Chang 2001; Tang et al. 2011; Venkatesan et al. 2000; Wu et al. 2009). There are some existing methods that are resilient to the rotation but provides the poor discriminative capability (Kozat et al. 2004; Lefebvre et al. 2002; Lei et al. 2011; Monga and Mihcak 2007; Roover et al. 2005; Swaminathan et al. 2006). Even though the ring partition and NMF based method (Tang et al. 2014) having good discrimination capability, is invariant to arbitrary rotation, yet it cannot localize the counterfeit region of the forged image. Localization of the counterfeit regions in the forged image is very useful in the medical and forensic evidence (Mishra and Adhikary 2013). This method also suffers from the convergence problem of the matrix factorization, which is based on the multiplicative update method. It is an extremely challenging task to design an image hashing technique that simultaneously satisfies all the requirements, such as rotation robustness, good discriminative capability, and localization of the forged area.
In this paper, a robust image hashing technique has been proposed to meet the above requirements simultaneously. In the proposed method, the secondary image obtained through ring portioning makes the system rotation invariant. The use of PGNMF instead of NMF helps the system to converge faster. The attribute of the PGNMF (i.e. learning parts-based representation) and use of the local features along with the global features helps to enhance the discrimination capability of the proposed algorithm. The combination of local features with the global features also makes the system capable of identifying the forged regions in the manipulated images.
Experiments were carried out on large image sets that were taken from standard image databases (USC-SIPI Image Database 2007; Ground Truth Database 2008) and downloaded from the internet. The experimental results show that the proposed algorithm provides better tradeoff amongst the rotation robustness, discrimination capability and localizing the counterfeit region.
The proposed image hashing involves four different steps as shown in Fig. 1.
Block diagram of the proposed image hashing
The input image is transformed to a normalized image size (256 × 256) with pre-processing. The final hash is formed by concatenating the two intermediate hash. First intermediate hash, is generated from the global features derived from the normalized image using ring partitioning and PGNMF. Second intermediate hash is generated from the shape-texture feature derived from the salient regions of the image (Hou and Zhang 2007). The final hash has been scrambled using the secret key K3. The PGNMF, ring partition, saliency detection scheme, local features and the proposed algorithm in details with a probable application have been discussed in the subsequent subsections.
PGNMF
Non-negative matrix factorization is an algorithm where a matrix is factorized into two matrices of lower dimension, with the property that all matrices have only positive elements (Paatero and Tapper 1994). Let \({\mathbf{L}}\) be a matrix of size m × n, where L ij ≥ 0, then it can be approximately represented by the two non-negative matrices \({\mathbf{W}} \in {\mathbf{R}}^{m \times p}\) (i.e. base matrix) and \({\mathbf{H}} \in {\mathbf{R}}^{p \times n}\) (i.e. coefficient matrix) by using NMF such as:
$${\mathbf{L}} \approx {\mathbf{WH}}$$
where p is the rank of NMF that should satisfy the condition \(p < { \hbox{min} }\left( {m,n} \right)\).
If each column of the matrix \({\mathbf{L}}\) represents the objects of an image, then NMF approximate it by linear combination of p basis columns in \({\mathbf{W}}\). There are various applications of NMF such as document clustering (Xu et al. 2003), finding basis vectors of the images (Lee and Seung 2001), and molecular pattern discovery (Brunet et al. 2003). The theoretical issues related to NMF scheme have been discussed in Donoho and Stodden (2004). The state-of-the-art NMF schemes have the convergence problem. To overcome this problem, Lin (2007) proposed a method, named PGNMF which has the faster optimization property. The illustration of PGNMF scheme is shown in Table 1.
Table 1 The PGNMF algorithm
Square image to secondary image (circular) conversion
Generally, image center remains fix after rotation manipulation. It has been observed that pixel values in different rings of the original image and its rotated version remain same as shown in Fig. 2a, b respectively. Hence, the square image can be converted into rings that may be subsequently used to construct the secondary image as shown in Fig. 3. Use of this concept makes the proposed system rotation invariant (Tang et al. 2014). The secondary image is constructed as follows:
Depicts the similar image information in rings of original image and its rotated version. a Original image. b 90° rotated image
Ring partition of a square image to construct secondary image. a Formation of annular rings (Red-Ring 1, Yellow-Ring 2, Green-Ring 3,...., Blue-Ring m). b Corresponding matrix representation
Let the size of a square image be n × n, the number of rings is m, and \({\mathbf{P}}_{i}\) be the set of pixel values in the i th ring \(\left( {i = 1, 2, \ldots ,m} \right)\). The inscribed circle area from a square image has been used for hash generation. The inscribed area has been divided into annular rings of equal area as shown in Fig. 3a. Each ring provides the column of the secondary image as shown in Fig. 3b. The radii of circles can be determined as follows: For an image of size n × n, the outermost radius is \(r_{m} = \left[ {\frac{n}{2}} \right]\). To find the other radii, firstly the area S enclosed by outermost radius is calculated and then the other ring area UA, i.e. average area is found as follows (Tang et al. 2014):
$$S = \pi r_{m}^{2}$$
$$U_{A} = \left[ {S/m} \right]$$
So, \(r_{1}\) can be computed by
$$r_{1} = \sqrt {\frac{{U_{A} }}{\pi }}$$
Thus, other radii \(r_{k} \left( {k = 1, 2, \ldots ,m - 1} \right)\), can be found using the following equation:
$$r_{k} = \sqrt {\frac{{U_{A} + \pi r_{k - 1}^{2} }}{\pi }}$$
The process of arranging the pixels from the rings (shown in Fig. 3a) into columns (shown in Fig. 3b) is as follows: Let the coordinates of the image center be (x c , y c ). If n is an even number, then, \(x_{c} = \frac{n}{2} + 0.5\) and \(y_{c} = \frac{n}{2} + 0.5\), otherwise, \(x_{c} = \frac{{\left( {n + 1} \right)}}{2}\) and \(y_{c} = \frac{{\left( {n + 1} \right)}}{2}\).
A circular region (\({\mathbf{C}}_{1}\)) (i.e. Ring 1) of radius r 1 around the center pixel has been selected as shown in (9)
$${\mathbf{C}}_{1} : \left( {x - x_{c} } \right)^{2} + \left( {y - y_{c} } \right)^{2} = r_{1}^{2}$$
where yth row and the xth column of the normalized image is given by \(1 \le x \le n, \;1 \le y \le n\).
The process of creating a mask \(({\mathbf{T}}_{1} )\varvec{ }\) for the first radius r 1 is given below:
First mask \(({\mathbf{T}}_{1} )\) is constructed having pixel value one (true) inside the ring of radius r 1 and pixel value zero (false) outside the circle as shown in Fig. 4.
First Mask \(({\mathbf{T}}_{1} )\) for \({\text{r}}_{1}\)
Let the pixel values of this region (i.e. Ring 1) be placed in the first column of matrix L by multiplying the input image \(\left( {\mathbf{I}} \right)\) and first mask \(({\mathbf{T}}_{1} )\):
$${\mathbf{L}}_{1} = {\mathbf{I}} \times {\mathbf{T}}_{1} ;\quad {\text{inside r}}_{1}$$
Similarly, other circles \({\mathbf{C}}_{k} \left( {k = 2,3, \ldots ,\,m} \right)\) can be constructed for r k (k = 2, 3, …, m).
$${\mathbf{C}}_{k} :\left( {x - x_{c} } \right)^{2} + \left( {y - y_{c} } \right)^{2} = r_{k}^{2}$$
The second mask \(({\mathbf{T}}_{2} )\) is constructed with pixel value one (true) for the pixels inside the annular ring of r 1 ≤ r ≤ r 2 and pixel value zero (false) outside the annular ring as shown in Fig. 5.
Second mask \(({\mathbf{T}}_{2} )\) for \(({\mathbf{r}}_{2} - {\mathbf{r}}_{1} )\)
Representation of saliency map
The pixel values of this region is placed (i.e. annular Ring 2) into the second column of matrix L by multiplying the input image \(\left( {\mathbf{I}} \right)\) and second mask \(({\mathbf{T}}_{2} )\):
$${\mathbf{L}}_{2} = {\mathbf{I}} \times {\mathbf{T}}_{2} ;\quad {\text{for}}\,(\varvec{r}_{2} - \varvec{r}_{1} )$$
Similarly, pixel values of other regions (i.e. annular Rings) are putted in the other column of matrix L:
$${\mathbf{L}}_{k} = {\mathbf{I}} \times {\mathbf{T}}_{k} ;\quad {\text{for}}\,(\varvec{r}_{k} - \varvec{r}_{k - 1} )$$
Thus, the rotation invariant matrix is obtained as follows:
$${\mathbf{L}} = \left[ {{\mathbf{L}}_{1} ,\;{\mathbf{L}}_{2} ,\;{\mathbf{L}}_{3} , \ldots ,\;{\mathbf{L}}_{m} } \right]$$
To compress a high dimensional vector, the matrix L is processed through PGNMF to obtain a compact form of first intermediate hash (Q).
Saliency map recognition
The salient region is defined as the visual attention of any image. For every image, there are two parts, an innovation and a prior knowledge (Hou and Zhang 2007). It is also called the novel and redundant parts. Redundant parts should be removed to get the novel parts of an image.
The log-spectrum, \(V\left( f \right)\) characterize the common information of an image. The redundant information is present in V(f) due to the fact that the log-spectra of different images are similar. The convolution of V(f) and an \(l \times l\) low-pass kernel is used to find the redundant information \(R\left( f \right)\), as given by (15)
$$R\left( f \right) = h_{l} \times V\left( f \right)$$
The novel portion (N(f)) of an image can be obtained by spectral residual method that can be derived by subtracting R(f) from V(f) and the salient map can be obtained by inverse Fourier transform of (f):
$$S_{m} \left( y \right) = {\text{F}}^{ - 1} \left[ {N\left( f \right)} \right] = {\text{F}}^{ - 1} \left[ {V\left( f \right) - R\left( f \right)} \right]$$
The salient region of an image can be obtained by taking the threshold three times of the mean of the \(S_{m} \left( y \right)\) (Zhao et al. 2013).
For human visual perception, the important local feature is the texture of the image. Six texture features have been proposed in Deselaers et al. (2004), Tamura et al. (1978). We have used three features, namely, coarseness (δ 1), contrast (δ 2), and kurtosis (δ 3). To calculate coarseness with respect to a particular pixel(i, j), the average of its neighborhood size (2k × 2k) is considered as follows:
$$A_{k} \left( {i,j} \right) = \frac{1}{{2^{2k} }}\mathop \sum \limits_{{x = i - 2^{k} }}^{{i + 2^{k} - 1}} \mathop \sum \limits_{{y = j - 2^{k} }}^{{j + 2^{k} - 1}} g\left( {x,y} \right),\quad k = 1,2, \ldots ,5$$
Here, \(g\left( {x, y} \right)\) represents the gray-level pixel. At every pixel(i, j), differences of average values in vertical and horizontal direction is as follows:
$$E_{k,h} \left( {i,j} \right) = \left| {A_{k} \left( {i + 2^{k - 1} ,j} \right) - A_{k} \left( {i - 2^{k - 1} ,j} \right)} \right|$$
$$E_{k,v} \left( {i,j} \right) = \left| {A_{k} \left( {i,j + 2^{k - 1} } \right) - A_{k} \left( {i,j - 2^{k - 1} } \right)} \right|$$
The S best (i, j) represents the highest difference value evaluated at(i, j), and is given by (20)
$$S_{best} \left( {i,j} \right) = \mathop {\arg \hbox{max} }\limits_{k = 0, \ldots ,5;d = h,v} E_{k,d} \left( {i,j} \right)$$
The coarseness (δ 1) for the image can be calculated as
$$\delta_{1} = \frac{1}{m \times n}\mathop \sum \limits_{i}^{m} \mathop \sum \limits_{j}^{n} S_{best} \left( {i,j} \right)$$
where, m and n represents the dimension of the image.
Image illumination changes are described as contrast. This can be evaluated using the variance (σ 2) and the fourth order moment (μ 4) of the gray value for the given region.
$$\delta_{2} = \sigma^{2} \mu_{4}^{ - 1/4}$$
The kurtosis which is used to calculate polarization of image can be measured as
$$\delta_{3} = \frac{{\mu_{4} }}{{\sigma^{4} }}$$
Brief description of the proposed image hashing
The detail of the proposed image hashing has been illustrated in Table 2.
Table 2 Proposed image hashing in details (From input image to generation of final hash)
The length of the final hash has been discussed in Table 3. It is observed that the final hash length is 106 integers. As the dynamic range of integer is [0, 255], therefore, it requires 8 bit to represent each integer value. Hence, the final length of the hash in bits is 106 × 8 = 848 bits.
Table 3 Structure of image hash
Authentication of images
For an image authentication, we have the reference hash \(\left( {{\mathbf{M}}_{0} } \right)\) of the original (reference) image, calculated hash (\({\mathbf{M}}_{1} )\) for the received (test) image. The above two hashes are compared to check whether the received image is similar/tampered version of the original image or simply the different one. A pair of visually similar images may have different pixel values due to the content-preserving operations. But visually same are called similar or same image. The process of image authentication is as follows:
Extracting the features The final hash for the test image is calculated using the proposed algorithm as discussed in Table 2 and let the hash be named as \({\mathbf{M}}_{1} = \left[ {{\mathbf{Q}}_{1} \,\varvec{ }{\mathbf{F}}_{1} \,\varvec{ }{\mathbf{Z}}_{1} } \right].\)
Separation of final hash The reference hash \(\left( {{\mathbf{M}}_{0} } \right)\) obtained from the original image is decomposed into the global and local feature vectors to obtain the components \({\mathbf{Q}}_{0} , {\mathbf{F}}_{{0,\varvec{ }}}\) and \({\mathbf{Z}}_{0}\)
Saliency map comparison The salient regions of the test image are compared with those of the original image using position vectors. If it is observed that the compared areas for a pair of salient regions are large enough, then it may be considered to be matched. Accordingly, the texture vectors are reshuffled by moving the matched components in each of the texture vector pairs \({\mathbf{Z}}_{0}\), \({\text{Z}}_{1}\).
Consider an example where, there are five salient regions in the reference image and four in the test image.
$${\mathbf{Z}}_{0} = \left[ {{\text{z}}_{0 }^{1} {\mathbf{z}}_{0 }^{2} {\text{z}}_{0}^{3} {\text{z}}_{0 }^{4} {\text{z}}_{0}^{5} 0 } \right]$$
$$\varvec{ }{\mathbf{Z}}_{1} = \left[ {{\text{z}}_{1}^{1} \varvec{ }{\mathbf{z}}_{1}^{2} {\mathbf{z}}_{1}^{3} {\text{z}}_{1}^{4} 0 0 } \right]$$
The first four pair of sub-vectors in \({\mathbf{Z}}_{0}\) and \({\mathbf{Z}}_{1}\) may be either match or unmatched. Accordingly, \({\mathbf{F}}_{0}\) and \({\mathbf{F}}_{1}\) are reshuffled.
Calculating Euclidean distance and decision making Euclidean distance between the image hashes is used to judge resemblance of the image pairs. Let us consider an image hash \({\mathbf{N}}\), which is structured by concatenating the global vector \({\mathbf{Q}}\) and the rearranged texture feature \({\mathbf{Z}}\), say \({\mathbf{N}} = \left[ {{\mathbf{Q}}, {\mathbf{Z}}} \right]\). Position feature \(\left( {\mathbf{F}} \right)\) is not required for distance calculation. However, it will be used during the counterfeit area localization. The Euclidean distance between reference hash vector \(({\mathbf{N}}_{0}\)) and the test hash vector \(({\mathbf{N}}_{1}\)) is defined as:
$${\text{D}} = \|{\mathbf{N}}_{1} - {\mathbf{N}}_{0}\|$$
The Euclidean distance is used to decide the similarities or dissimilarities with respect to the pre-determined threshold. In practice, the global feature of an image obtained by applying Ring partition-PGNMF approach is adequate enough to distinguish the similarities/dissimilarities of the image pairs. The inaccuracy of the saliency detection may have an undesirable influence on the calculation of Euclidian distance that may lead to erroneous results. To overcome this problem, the hash distance may be calculated based on global features only to detect the similarities/dissimilarities in the decision-making process. This is given as follows.
$${\text{D}} = \|{\mathbf{Q}}_{1} - {\mathbf{Q}}_{0}\|$$
The experimental results for the robustness, discriminative capability, and counterfeit area localization have been discussed in the next subsections.
Robustness evaluations are quantitative analysis for the normal content-preserving operations. An image hash should be approximately same after any non-malicious activity. These activities consist of image rotation, JPEG compression, watermark embedding, Gaussian low-pass filtering, gamma correction, brightness adjustment, contrast adjustment, and image scaling (Swaminathan et al. 2006).
We have tested the proposed model using 200 different images taken from various sources (USC-SIPI Image Database 2007; Ground Truth Database 2008). Because of space limitation, we have used only five images (Peppers, Airplane, House, Lena, and Baboon) for visual representation in the paper. Content preserving operation is created by StirMark 4.0 (Petitcolas 2000), Photoshop CS3 (10.0), and MATLAB (2013a) as shown in Table 4.
Table 4 Content-preserving operations with given parameter values
The Euclidean distance between the original image and the image after content preserving operations is shown in Fig. 7. It may be observed that if we choose the threshold of Euclidean distance as 7, then few perceptually similar image (i.e. 3/200 × 100% = 1.5%) may be considered as the different image, as shown in Fig. 7 (rotation operation). Therefore, the true rejection is 1.5%. However, the false acceptance is zero percent.
Robustness performance of the proposed algorithm for some content preserving operations
Different image samples used in discrimination test
The Euclidean hash distances for visually different images are almost greater than 10 as shown in Fig. 9. If we increase the selected threshold of the Euclidean distance to 10, the true rejection is zero percent. Nevertheless, there are few cases (0.116%) in which the Euclidean distance is below 10 for different images. Thus, the false acceptance is 0.116%. Hence, it may be concluded that the threshold of the Euclidean distance may be selected according to the application of an image hashing.
Discrimination test based on 300 different images
For this study, we have used an image hashing technique for an image authentication purpose. The basic requirement of an image authentication is to have zero false acceptance rate. Because of this, we have selected a threshold Euclidian distance 7 for subsequent analysis. The experimental results which are shown in Fig. 7 demonstrates the effectiveness of the proposed method. It can be observed from Fig. 7, the Euclidean distances of some content preserving operations are less than the selected threshold (i.e. 7) except few cases in rotation operation. However, these few cases in rotation operation may be considered similar image at selected threshold 10, but with the cost of small false acceptance. Hence, the proposed method is robust to some content preserving operations.
Discrimination capability
To check the discriminative capability of the proposed method for visually different images, we have taken 300 images from different sources. Some of the sample images (animal, car, house, scene and lady, etc.) from the 300 image set have been shown in Fig. 8. The hash vector for the 300 images has been generated using the proposed method and the calculated Euclidean distance for different possible combinations of hash vector pairs are shown in Fig. 9. It can be observed that the obtained Euclidean distances are above 7 (i.e. pre-determined threshold) in all the cases. This signifies that the proposed method provides satisfactory discrimination.
Testing for counterfeit area localization
In addition, we have carried out the experiments for malicious activities and localization of the counterfeit area. Whenever a particular image is attacked by the malicious operation, such as an insertion, deletion, and replacement, etc., it looks perceptually different (Swaminathan et al. 2006). Then, the obtained image is called a forged image and should provide different hash values. The forged images are generated using Photoshop. Some examples of the original and forged images are shown in Fig. 10. The experimental results of forged area localization have been shown in the third column of Fig. 10. In Fig. 10c, the green rectangle is the forged area, in which some content that was there in the original image has been removed in forged image. Similarly, green rectangle in Fig. 10f, i shows the forged area localization for content insertion and object replacement respectively. We have generated the hash vectors of original images and forged images, and calculated the hash distances which are shown in Table 5. It is observed that all the hash distance are more than the pre-selected threshold. Hence, it may be concluded that the proposed method is sensitive towards malicious changes made in the image.
Forged area localization. a, d, g Original images. b, e, h Forged images. c, f, i Green rectangle signifies the localized forged area
Table 5 Image hashes for tampered image
The proposed algorithm has been compared with some of the well-known methods available in the literature (Ahmed et al. 2010; Khelifi and Jiang 2010; Monga and Mihcak 2007; Tang et al. 2008; Zhao et al. 2013). The performances have been evaluated using Receiver Operating Characteristics (ROC). The ROC is a characteristic curve plotted using false positive rate (FPR) along the abscissa and true positive rate (TPR) along the ordinate. The FPR and TPR are calculated as follows:
$${\text{FPR }} = \frac{{ \in_{1} }}{{O_{1} }}$$
$${\text{TPR }} = \frac{{ \in_{2} }}{{O_{2} }}$$
where, \(\in_{1}\) represents the number of visually different image pairs judged as similar image pairs and \(\in_{2}\) represents the number of perceptually identical image pairs judged as similar image pairs. The O 1 and O 2 denote the total number of visually dissimilar image pairs and a total number of perceptually similar image pairs respectively. The ROC comparison amongst the different algorithms is shown in Fig. 11. It can be observed from Fig. 11 that the proposed method TPR is higher especially at low FPR as compared to the other methods.
ROC curve comparisons of the different algorithms
The performance comparison of the proposed method with some of the state-of-the-art methods are shown in Table 6. The methods proposed in Ahmed et al. (2010), Monga and Mihcak (2007), Tang et al. (2008) are not robust to the rotation operation. This may be due to the block-based operations carried out for feature extraction. The hashing methods proposed in Tang et al. (2008) and Monga and Mihcak (2007) fails to localize the forged area in the image. The method proposed in Khelifi and Jiang (2010) is robust to content-preserving operations but fails to find the small counterfeit area. The method proposed in Zhao et al. (2013) is robust against almost all the geometric operations but not robust to rotation operation beyond 5°. For ROC comparison, we have used only the baseline methods (used in this study) as described in Monga and Mihcak (2007), Tang et al. (2008), Zhao et al. (2013).
Table 6 Comparison of proposed method with some of the state-of-the-art methods
It may be observed from Table 6 that the proposed algorithm is robust against the image manipulation and possesses good discriminating characteristics. Moreover, it can locate the counterfeit area with sufficient accuracy. Table 6 shows that the proposed method TPR (i.e. 0.9811 with zero FPR) and FPR (i.e. 0.0012 with TPR one) is higher and lower respectively as compared to other methods. The limitation of the proposed algorithm is that full image information has not been used for hash generation. Around 21% of the image information remain unused during the conversion from a square image into a secondary image. The other drawback is that the forged area cannot be accurately located under simultaneous rotation and malicious operation being carried out in the image.
The computational complexity of the proposed method and some of the state-of-the-art methods have been evaluated on a personal computer (with Intel core i7, 3.40 GHz, 8 GB RAM) running MATLAB 2013a. The comparison is shown in Table 6. It is observed that the proposed method is computationally efficient as compared to some of the existing methods. The reason for increased computational complexity as compared to the system proposed in Tang et al. (2008) may be due to the use of local features along with global features. Even though the computation complexity is not mentioned in Ahmed et al. (2010), it may be inferred that the major computation load is not because of wavelet transform but due to determining the decision threshold using various image processing operations.
In this paper, we have proposed a robust image hashing based on global features extracted through ring partition-PGNMF and local features that consist of position and texture information. The combination of global and local features is robust against the content-preserving operations, which has a desirable discriminative capability also. The local feature is useful to localize the forged area. The enhancement of the discriminating capability of proposed hashing is due to PGNMF because it is good at learning part based representations. The PGNMF converges faster as compared to other NMFs. Experiments have been carried out on large image dataset to evaluate the performance of proposed hashing method. It is observed that proposed image hashing technique is robust to content preserving operations. It has also the capability to localize the forged area with sufficient accuracy.
The proposed algorithm may not be able to detect the color changes in the image. In future, the efficient color feature may be considered to detect the color forgery. Forged area localization under simultaneous rotation and malicious operations may also be explored. The other limitations such as reduction of hash length and computational complexity may be addressed during the future scope of work.
DWT:
discrete wavelet transform
DFT:
discrete Fourier transform
MD:
SHA:
secure hash algorithm
DCT:
discrete cosine transform
NMF:
non-negative matrix factorization
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RKK and RHL carried out the study and implementation of Ring partitioning and PGNMF, participated in the sequence alignment and drafted the manuscript. BBR carried out the study and implementation of the spectral residual method. All authors read and approved the final manuscript.
The authors would like to acknowledge people of Speech and Image Processing Laboratory, National Institute of Technology Silchar, India for providing support and necessary facilities for carrying out this work. The authors are thankful to Mr. Amarjit Roy, Ph.D. Scholar, Department of Electronics and Communication Engineering, for his constructive criticism and discussion.
This study has not received any funding.
Department of Electronics and Communication Engineering, NIT Silchar, Silchar, Assam, 788010, India
Ram Kumar Karsh & R. H. Laskar
Department of Electronics and Communication Engineering, NIT Mizoram, Aizawl, Mizoram, 796012, India
Bhanu Bhai Richhariya
Ram Kumar Karsh
R. H. Laskar
Correspondence to Ram Kumar Karsh.
Karsh, R.K., Laskar, R.H. & Richhariya, B.B. Robust image hashing using ring partition-PGNMF and local features. SpringerPlus 5, 1995 (2016). https://doi.org/10.1186/s40064-016-3639-6
Image hashing
Multimedia security
Ring partition
Saliency detection


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    {
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redpajama_set_name: RedPajamaGithub


Hello, everyone. Welcome to episode 183 of Against the Odds. Last week, we had an all-Kamigawa Against the Odds poll, and the results were unsurprising in the end: One with Nothing—a card considered to be among the most useless in the entire history of Magic—took home a crushing victory. As such, we're heading to Modern this week to play a deck looking to discard its hand to One with Nothing. Then, at the end of today's article, we'll be running an all-Kamigawa poll minus One with Nothing to give the other poor Kamigawa cards a chance at glory. As for the deck itself, the main plan is to use One with Nothing as a way to stock our graveyard with threats and also cast a bunch of madness burn spells on the cheap, which in turn will allow us to get our threats back from the graveyard and (hopefully) kill our opponent as early as Turn 3. If all else fails, we can discard our hand and use Infernal Tutor as a Modern-legal Demonic Tutor to find whatever we need for a given situation. Is it possible to win a game by discarding our entire hand to One with Nothing in Modern? Is there any chance the one-mana instant is better than people think? Let's get to the video and find out; then, we'll talk more about the deck!
When One with Nothing won the poll, my initial idea was to take advantage of some of the "whenever you discard a card" creatures from Amonkhet like Shadowstorm Vizier. However, after playing several games with the deck, I realized that One with Nothing simply didn't do enough in the build, often making us discard our hand for the equivalent of casting a Giant Growth on Shadowstorm Vizier. As such, it was back to the drawing board, where I eventually ended up with the build we're playing today: a sort of weird, madness-y Arclight Phoenix–, Bloodghast-, and Prized Amalgam–fueled deck. While not all that consistent, the deck can be quite explosive when everything comes together, potentially winning the game as early as Turn 3 with the help of One with Nothing!
The upsides of One with Nothing are that it's cheap at one mana and instant speed. The downside is that the effect of One with Nothing—discarding your hand—is actually something you'd rather not have in 99.9% of decks, which makes it a challenging card to build around. For One with Nothing to work, we need a weird combination of cards that actively want to be in our graveyard and cards that are better when we discard them from our hand. Even then, One with Nothing can be pretty painful in some situations since it's an all-or-nothing proposition—we can't just discard the part of our hand that we want in the graveyard or to madness; we have to discard the entire thing. Thankfully, with some careful deck building, there are some ways to (sort of) harness the power of One with Nothing.
First and foremost, we have three creatures that actively want to be in our graveyard since they can return to the battlefield for free. While they all work slightly differently, in the end, they are similar: after we discard them to One with Nothing, they will (hopefully) reanimate themselves to kill our opponent. Arclight Phoenix is the hardest to return from the graveyard since we need to cast three spells in one turn, but it's also the most powerful, giving us a hasty 3/2 flier. Thankfully, we have some One with Nothing tricks to get it back from the graveyard, which we'll talk about in a minute. Meanwhile, Bloodghast reanimates itself whenever we make a land drop, which means we can discard our hand to One with Nothing with an uncracked fetch land on the battlefield and then return Bloodghast by cracking the fetch. Finally, Prized Amalgam is basically along for the ride. While it can't return itself from the graveyard, whenever we get back an Arclight Phoenix or Bloodghast from the graveyard to the battlefield, all copies of Prized Amalgam follow at the end of the turn, giving us some extra free bodies on the battlefield.
When it comes to getting Arclight Phoenix back from the graveyard, we have two plans involving One with Nothing. The first takes advantage of the fact that One with Nothing is an instant and works sneakily well with Infernal Tutor. If you're not familiar with Infernal Tutor, it's basically a bad tutor unless we're hellbent, when it turns into a Modern-legal version of Demonic Tutor. The plan here is simple: we cast Infernal Tutor but maintain priority, so while Infernal Tutor is on the stack, we can cast One with Nothing to discard our hand, which then turns Infernal Tutor into Demonic Tutor since we're empty-handed when it resolves. In theory, we can then tutor up any card from our deck. But we often want Noxious Revival, which we can cast for its Phyrexian mana cost, putting Infernal Tutor back on top of our deck for the next turn while also counting as our third spell for the turn to get back all of our Arclight Phoenixes and Prized Amalgams. When we have the right draw, we can do this all on Turn 3, potentially getting back up to 12 power of Arclight Phoenixes after discarding them to One with Nothing!
Plan B for returning our Arclight Phoenixs and Prized Amalgams on Turn 3 is using One with Nothing to discard Fiery Temper and [[Alms of the Vien], both of which we can cast for just a single mana if we discard them, thanks to their madness cost, to hit our opponent for three damage. As such, a hand with a bunch of Arclight Phoenixes, Bloodghasts, and Prized Amalgams plus One with Nothing and any two of Fiery Temper or Alms of the Vein ends up giving us a Turn 3 where we dome our opponent for six and also get back a bunch of creatures from the battlefield to finish our opponent off over the next turn or two.
Faithless Looting and Burning Inquiry give us some backup ways to fill our graveyard in the early turns while also digging through our deck to find One with Nothing, Infernal Tutor, and our other key cards. We've talked a lot about trying to kill our opponent on Turn 3 with the help of One with Nothing, and this gets even easier when we have cards like Faithless Looting and Burning Inquiry to start stocking our graveyard with recursive threats on Turns 1 and 2.
Finally, Shadow of the Grave is just a one-of, but it has some unique card-advantage-generating potential in the deck, especially in the late game. Once we have four or five lands on the battlefield, it's possible that we can cast multiple copies of Faithless Looting and Burning Inquiry in a turn and then get back all of the cards we discarded, with the help of Shadow of the Grave, making it a solid one-of tutor target for Infernal Tutor. The reason we don't have more copies is sometimes we don't actually want to get all the cards we discarded back from our graveyard to our hand since much like One with Nothing, Shadow of the Grave is an all-or-nothing affair, so we'd have to return the recursive creatures that we wanted to discard along with all of the utility and support cards that we'd like back in our hand. Still, as a one-of, Shadow of the Grave has enough situational value that it's worth a slot in our deck, especially since we can tutor it up in situations where it is good.
In theory, One with Nothing can beat just about any deck in Modern if it gets a solid Turn 3 combo draw. On the other hand, it does struggle with a couple of things. First and foremost, the deck is extremely reliant on the graveyard, and the Modern format is pretty prepared for graveyard decks. This means even when we get a nut draw, we occasionally still lose because our opponent has Ravenous Trap or Surgical Extraction to exile our graveyard. Second, we don't really have removal in our graveyard, and while our creatures are good at attacking, they aren't very good at playing defense, which means we sometimes just get run over by aggro decks that curve out starting on Turn 1, especially if we don't have our most optimal draw.
All in all, we played five matches with One with Nothing and ended up winning two, good for a 40% match win percentage, putting One with Nothing solidly (although not massively) below-average for an Against the Odds deck. That said, perhaps our most spectacular games ended up as losses, such as our last match versus Grixis Control, where we had some really frightening One with Nothing combo draws but our opponent had graveyard hate at the ready to blow us out. It's hard to hold that against One with Nothing specifically since the same thing would have happened no matter how we were stocking our graveyard. So rather than being a mark against One with Nothing, it's more of a testament to Modern being extremely ready to fight graveyard decks. Speaking of One with Nothing, the card itself is extremely high variance. In some games, we literally can't cast it or we'll basically lose the game on the spot. In others, it enables some extremely powerful game-ending combos. The biggest problem is how all or nothing the card is. A version of One with Nothing that allowed you to discard any number of cards from your hand would probably be very playable and maybe even broken in some decks. But as is, the card is either broken or uncastable while also lining up poorly with the Modern format as a whole. Still, heading into our matches, I thought we might not win a game with One with Nothing, so winning 40% and losing a couple of games that we would have won if our opponent didn't have graveyard hate is a pretty solid showing for the card in a format as powerful and hateful as Modern!
Last week's poll wasn't especially fair: everyone voted for One with Nothing so none of the other sweet Kamigawa cards really had a chance. As such, today we're going to give the non-One with Nothing cards from Kamigawa a second change. Along with a couple returning options from last week poll, we also have some fun new additions. Which of these janky Kamigawa cards should we play next week? Let us know by voting below!


In a 1980 referendum, the majority of Swedes called for phasing-out nuclear, but ever since then each government has hemmed and hawed without actually closing many reactors (of 12 total reactors, 2 have been closed). Now the tides of public opinion have turned, and a majority favors keeping nuclear, which is considered to be a cleaner and less carbon intensive energy source than coal or gas. Swedes also believe that their reactors are safe, and their waste management superior. Some scary near accidents at Swedish reactors within the last two years didn't even raise many eyebrows.
There are some political reasons why now is the time the Swedish government has made a stand. Sweden will assume the EU presidency this fall and needed to have a coherent energy policy among the four-party center-right Alliance currently in power. That's the first step to getting a national energy policy together (due to the EU in 2010) describing how Sweden will meet the 49% renewables goals by 2020 mandated by the EU Renewable Directive.
"The nuclear question effects all our energy decisions. In Sweden it's always wind against nuclear. Wind can't replace nuclear - it's 65 TWh that we can't just do without. However, if we want to build CO2-free going forward we also need wind. Taking the middle road...that would be such a relief."
Sweden Swapping Efficiency for Security?


Last year I had the chance to try out and review Swiftwick's Aspire line of compression socks. I loved the socks and was excited to get the chance to review another pair of Swiftwick socks after this year's InterBike. After the show, Swiftwick sent a sample of their brand new Medical Recovery+ socks for testing which I have been wearing for a few weeks now.
The Medical line is Swiftwick's newest sock on the market. It is the first athletic recovery sock in the industry to achieve a medical class II rating. The socks have several applications; travel comfort, sport recovery, swelling, and Deep Vein Thrombosis can all be addressed with the Recovery+.
While somewhat counter-intuitive, it is important to point out that cyclists, as endurance athletes, can be at an increased risk of developing DVT. The condition occurs when a blood clot forms in the extremities (often in the legs). This is a very dangerous situation that, if not recognized and treated, could potentially lead to death.
Endurance athletes have a unique risk of developing DVT. There are risk factors that you might not even consider, after all we are fitter than most of the rest of the population so how could we possibly be at risk for something that is usually associated with sedentary lifestyles?
The athlete is fit, with a low resting heart rate. In fact, when in peak shape, this athlete's resting heart rate is in the 40s. The cyclist arrives at his race after a 3 hour drive and prepares for the event. He finishes his race but during his cool-down he takes a spill. It is a mild fall with no injuries other than to his ego. He packs up his car and heads out on his 3 hour journey back home. Even though he is a bit dehydrated from the day's effort, he is in a rush to get back home to his family so he pushes through without stopping to grab a drink. On Monday he returns to work, sitting at his desk most of the day.
In this scenario there are plenty of risk factors present that could increase the athlete's risk of DVT. Sitting for long periods of time, dehydration, trauma from a fall, and even the low heart rate (slower blood flow) all put him at greater risk of developing DVT.
So how are compression socks supposed to help prevent this potentially serious condition? The Recovery+ socks are made with graduated compression. Simply stated, the greatest amount of compression is built into the sock at the arch of the foot. The sock's compression gradually decreases up the leg. This aids in returning the blood from the leg to the heart. Swelling and discomfort can also be relieved with the use of the compression socks.
Recovery after a hard race or ride is very important for athletes. It is in the recovery phase that we actually get stronger. Therefore, anything that assists or speeds up your recovery is very beneficial for performance and training.
When used for recovery purposes, compression socks increase circulation throughout the leg. Increased blood flow means increased oxidization of the tissue resulting in better and speedier recovery. The reduction in swelling and soreness are additional benefits for athletes recovering from hard workouts.
Like other Swiftwick socks I have worn, these are very comfortable. They have the same foot-bed as their other socks and the 375 needle-count produces a tight weave which is smooth, soft, and durable. Like a high thread-count bed sheet, they feel great on your feet. The compression can be felt throughout the sock but is not restrictive and they are a bit easier to get on and off than the Aspire compression line (which are made for performance so their compression is slightly different). In addition, the socks are antimicrobial which helps keep the socks smelling fresh.
While purely anecdotal, I have found that my recovery does seem to be a little better between workouts if I have worn the Medical Recovery+ socks between workouts. I have worn them during the day, overnight, and immediately after workouts.
I can say that between the research, my previous experiences with Swiftwick products, and my own experience with these compression socks, I would not hesitate to recommend these to athletes looking to improve their comfort, recovery, and prevent possible complications from DVT.
The Recovery+, which retails for 69.00, was created specifically with athletes in mind. They also have the Health+ sock which is designed for people who are not as active and are meant to be worn for shorter periods of time. the Health+ retails for 24.99.
We have a big giveaway coming up this holiday season and our readers will have their chance to win a pair of these socks of their very own. Check back for details but if you want to win, you should follow us on Facebook and Twitter for your chance to win one of the great prizes.


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Q: Add hover text or text annotation in plotly gauge chart Can we add hovertext or text annotation in a plotly gauge chart? For example in my plot below I want to add in the green area hover or text or both "Uptake first dose%: 19.8" and in the gray area "Not vaccinated (%):80.2"
library(plotly)

fig <- plot_ly(
  type = "indicator",
  mode = "gauge+number+delta",
  value = 19.8,
  title = list(text = "Uptake first dose %", font = list(size = 24)),
  delta = list(reference = 70, increasing = list(color = "gray")),
  gauge = list(
    axis = list(range = list(NULL, 100), tickwidth = 1, tickcolor = "lightgreen"),
    bar = list(color = "lightgreen"),
    bgcolor = "white",
    borderwidth = 2,
    bordercolor = "gray",
    steps = list(
      list(range = c(0, 50), color = "lightgreen"),
      list(range = c(20, 100), color = "gray")),
    threshold = list(
      line = list(color = "black", width = 4),
      thickness = 0.75,
      value = 70)))
fig <- fig %>%
  layout(
    margin = list(l=20,r=30),
    paper_bgcolor = "lavender",
    font = list(color = "darkblue", family = "Arial"))

fig

A: You can add annotations but I couldn't find a way to get the hovertext without an annotation.
fig <- plotly::plot_ly(
  type = "indicator",
  mode = "gauge+number+delta",
  value = 19.8,
  title = list(text = "Uptake first dose %", font = list(size = 24)),
  delta = list(reference = 70, increasing = list(color = "gray")),
  gauge = list(
    axis = list(range = list(NULL, 100), tickwidth = 1, tickcolor = "lightgreen"),
    bar = list(color = "lightgreen"),
    bgcolor = "white",
    borderwidth = 2,
    bordercolor = "gray",
    steps = list(
      list(range = c(0, 50), color = "lightgreen"),
      list(range = c(20, 100), color = "gray")),
    threshold = list(
      line = list(color = "black", width = 4),
      thickness = 0.75,
      value = 70)))
fig <- fig %>%
  plotly::layout(
    margin = list(l=20,r=30),
    paper_bgcolor = "lavender",
    font = list(color = "darkblue", family = "Arial"),
    annotations = list(x = 0.05, y = 0.3, text = "19.8%",
                       hovertext = "Your Text",
                       showarrow = FALSE))

fig

You could also use add_annotations if you want to add multiples.
See https://plotly.com/r/text-and-annotations/

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Jerzy Skolimowski
Jerzy Skolimowski (Polish: [jɛʐɨ skɔliˈmɔfskʲi], born 5 May 1938) is a Polish film director, screenwriter, dramatist and actor. A graduate of the prestigious National Film School in Łódź, Skolimowski has directed more than twenty films since his 1960 début Oko wykol (The Menacing Eye). In 1967 he was awarded Golden Bear for his film Le départ. His famous film is Deep End(1970). Jane Asher and John Moulder Brown acted in Deep End. He lived in Los Angeles for over 20 years where he painted in a figurative, expressionist mode and acted occasionally in films. More recently, he returned to Poland, and to film making as a writer and director after a 17-year hiatus with Cztery noce z Anną (Four Nights with Anna) in 2008. He received the Golden Lion Award for Lifetime Achievement at the 2016 Venice Film Festival.
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Airstrike target Haqqani network compound in Kurram Agency of Pakistan
By Khaama Press / in Afghanistan / on Thursday, 30 Nov 2017 02:52 PM / 0 Comment / 632 views
An airstrike has been carried out on the compound of a commander of the notorious Haqqani terrorist network in the tribal regions of Pakistan, it has been reported.
According to the local media reports in Pakistan, the airstrike was carried out earlier today in the vicinity of Kurram Agency in Federally Administered Tribal Region of Pakistan.
The reports further add that three people were killed in the attack on commander Rashid's compound which is located in Ghauz Garhi area.
The Pakistani government officials have not formally commented regarding the airstrike so far.
The latest airstrike targeting the compound of Haqqani network commander takes place amid growing pressures on Pakistan regarding the terror safe havens, particularly the Haqqani network sanctuaries, which the officials are saying have remained intact despite the major operations launched by the military of the country.
The US President Donald Trump announced the new strategy of Washington regarding South Asia in mid-August this year and criticized Pakistan for remaining reckless to act against certain terror groups still operating in its soil.
The top US commander in Afghanistan Gen. John Nicholson on Tuesday said that the leadership of the Taliban group still resides in Pakistan.
Khaama Press
The Khaama Press News Agency is the leading and largest English news service for Afghanistan with over 3 million hits a month. Independent authors/columnists and experts are welcome to contribute stories, opinions and editorials. Send stories to [email protected]
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Head of the Chamber of Mines and Industries of Afghanistan Sherbaz Kaminzada said that factories...
Taliban deny Kamal Khan Dam's water flowing to Iran
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Six people including IEA local commander killed in gunfire
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More and more small businesses are making their Instagram accounts private to gain control over their followers, fan and to choose quality content over quantity. Why should you make your Instagram account private? There are a few different benefits to doing this. San Diego Marketing is here to help you weigh all of the pros and cons before trying it out for yourself.
One of the first reasons to make your Instagram account private is to create more personalized and private content. Brands are moving toward a more personalized experience is which is why users are leaning towards small, closed groups. This is part of the reason why Facebook groups are becoming so popular right now. When you make your Instagram private, your followers will limit the number of people who can view your posts which shows your followers that you value quality overreach. This not only helps your followers feel special, but it makes your content feel more tailored to them.
If you put a bouncer in front of the door to your content, isn't that going to make more people curious about what you're posting? When you make your Instagram account private, this gives your loyal followers a first-hand look at what's new with your business. In addition, it will make non-followers interested in what you might be posting.
Another benefit of making your Instagram account private is that it might help you increase your followers. Making your Instagram private will increase your followers because the sense of exclusivity will make people want to follow you. In addition, if a user sends one of your posts to a friend, they won't be able to see it without following you.
To start, any of your followers who try to unfollow you will receive a notification asking if they are sure that they want to unfollow you. This will help you reduce the amount of unfollows for your page.
Another reason to make your Instagram private is to gain more control over your account. When your account is on private, it's easier to weed out bots, trolls, and bad followers. You can pick and choose who you want to accept to follow you. If you have a particular audience you are trying to reach, putting your account on private is one of the best ways to achieve your ideal audience.
Of course, there are a few reasons you shouldn't put your account on private. First, none of your posts will be searchable. This means that if you hashtag your posts, nobody will be able to find the post in the search function because your account is on private. There is also the possibility that people don't want to follow you because you're not on public. But are these the real followers you want? The last reason you might not want to put your account on private is that you need to switch it to a personal account to do so. This means that you lose all analytics and other business page features.
If you're curious to see the results that putting your account on private can bring, try it out for yourself. If you don't like it, switch back. Who knows? This might be the perfect way to grow your Instagram with the best audience possible. Contact San Diego Marketing to learn more about Instagram marketing.


text:
Black Lives Matter Business Diversity, Equality, Inclusion News Politics
Hundreds of Companies Unite To Oppose Voting Restriction Bills, Thanks to Black Executives
by Derek Major April 14, 2021 April 14, 2021 1817
More than 100 companies are fighting voting suppression bills that are being introduced in states across the country. Image: NAACP
Hundreds of the country's top companies and corporate executives have signed a statement released Wednesday opposing "any discriminatory legislation" making voting harder for Americans.
The statement and signatures represent the most aggressive action yet against Republican efforts to restrict voting access to ensure victories at the polls. Since former President Donald Trump's election loss, more than 100 bills restricting voting have been introduced in 48 states.
Last month Georgia Gov. Brian Kemp signed into law the most restrictive voting bills since the Jim Crow era.
The statement was organized by Kenneth Chenault, a former executive at American Express and Kenneth Frazier, the chief executive of Merck. Additionally, the statement was printed in Wednesday's New York Times and Washington Post.
Late last month, Chenault and Frazier led a group of Black executives in calling on more corporations to join the effort and oppose voting restriction bills being introduced across the country. Charles Phillips, co-founder of the Black Economic Alliance told BLACK ENTERPRISE several civil rights organizations came to them for help and awareness and they quickly accepted.
"This is the second ad which is much larger than our first one," Phillips said. "It started when a civil rights organization reached out to a couple of Black executives in myself, Chenault and Frazier and said we're trying to fight these laws but there seems to be more coming in other states but this one is here and now and we've run out of options. We need your influence."
Phillips added the call took place Sunday and by Tuesday more than 72 Black executives signed up and ran the first ad. Since that first ad was released, a number of other top companies have voiced support for the effort. These include Amazon, General Motors, Netflix, Starbucks, and many others.
The statement does not address any specific legislation that has been introduced in a state and Chenault told the Times, there was no expectation for companies to oppose individual bills. Frazier added that this is not a partisan effort and that protecting voting rights should be both a Democratic and Republican effort.
The issue, however, isn't expected to go away soon and big business could be caught in the middle. After Delta Air Lines opposed the voting restriction bill passed in Georgia, lawmakers in the state threatened to rescind a tax break that saves the airline millions of dollars.
When Major League Baseball announced it would move its All-Star Game from Atlanta to Colorado over the Georgia voting restrictions bill, a group of Republican senators threatened to strip the league of its antitrust exemption.
Black Americans in general view voting differently than white Americans or other races. For Black Americans, the road to gain voting rights in this country is painted with blood due to efforts to prevent Black Americans from voting. That includes the three-fifths rule, Jim Crow laws, and outright intimidation through violence.
"That was one of the motivating factors in this effort," Phillips told BLACK ENTERPRISE. "Too many people fought too hard and gave their lives to vote for us to start going in the other direction. If we can stop that, we should. We can't let the gains of the last 50 years go in the other direction."
The group doesn't have any additional actions at this point but that could depend on the outcome of the bills.
Derek Major


This Rakhi, make these easy dessert recipes at home and give your kids another reason to cheer this day!
If you think there's no such thing as too much chocolate, then you've definitely come to the right place. Chocolate is sinful pleasure, celebratory, intoxicating, comforting and all things heavenly! If there is any problem in the world, there is always a chocolate to push it to later – and, going by medical research, even solve it!
Fit for a festive day, but made without much sweat! There aren't too many people who will be able to resist these heart-melting chocolate recipes from Chef Neha Lakhani's Patisserie Royale!
Get your aprons on and the ovens heated up!
These can be enjoyed on their own or used to decorate cakes and other desserts.
Boil the heavy cream and pour over dark chocolate and mix to make a ganache. Add coffee flavoring according to desired taste.
Let this mixture cool to room temperature and then chill it in refrigerator for 20 to 30 minutes till it is a little firm.
Make balls of 20 grams of this ganache and press them to look like a disc and let them chill again for 30 minutes.
On a double boiler, melt the dark chocolate for dipping.
Temper the chocolate and then dip each ganache disc into it using a dipping fork and place it on a silicon mat until it sets.
Finally dust some gold shimmer powder on it for final touches. Take care to store in a cool room till you serve.
This is an ideal breakfast – more so for fussy kids who drive you nuts over nuts! Make a batch of this and you can store it in an airtight container in the refrigerator.
Half cup seeds of your choice – pumpkin seeds are a great options. You could also sunflower, flax (for great digestion), and sesame seeds. Just don't add TOO many different kinds to avoid giving the granola a confused flavor.
Mix all the ingredients thoroughly in a bowl and put on a baking tray.
Bake for 20 minutes till desired golden color is attained. Let it cool completely.
Sprinkle some dried or candied fruit (apricot, peach pieces or cranberries) and the chocolate chips and enjoy with yogurt or milk or just as a snack.
Chocolate truffles are one of those things that looks really hard to make, but this recipe, that needs five ingredients and about 40 minutes to make, will be your little secret!
Boil the cream and add it to the chocolate and mix. Let it cool for a bit. Then add the lemon juice and zest once the ganache is a little cool. Allow it to harden in the refrigerator slightly. Roll this mixture into small balls and refrigerate for another 15 minutes to firm them up.
Dip the firm lemon ganache balls into chocolate and roll them over cocoa powder. Allow to set on butter paper in the refrigerator.


text:
August - 2018 special2 issue
Shivlingeshwar College of Pharmacy
By Dharashive V. M, Principal
Dharashive V. M
Dharashive V. M., Principal
Completed Graduation in Pharmacy from Shivlingeshwar College of Pharmacy, Latur in 2009 and Post-Graduation from Nargund College of Pharmacy, Bangalore in 2011, Dharashive V. M. is currently serving as the Principal at Shivlingeshwar College of Pharmacy. He is also pursuing Doctorate from Swami Ramanand Teerth Marathwada University, Nanded.
Latur district in Maharashtra is infamously known as a town that kept its water queues in order by the instrument of Section 144. In fact, an entire train had been exclusively arranged to ply water to these parts of the country. In 2015, the drought was at its peak and many famers of this agricultural district struggled to meet the day-to-day expenses. Shivlingeshwar College of Pharmacy located in a rural area of Latur district was one among the few establishments that helped the people of Latur in its toughest of the times. "Many of our students were unable to pay their fees, as most of them were from farming background. In such difficult situation, we decided to avail student fees by providing them scholarship of more than Rs. 15 lakh," recalls Dharashive V. M., Principal, Shivlingeshwar College of Pharmacy.
Owing to the demonstrated academic excellence, it took not much time for the college to carve a niche for itself in the pharmacy education arena. Being the first private college to conduct and run six years integrated course of Pharm D. successfully in the region, the college also offers B. Pharm course which is affiliated
to Swami Ramanand Teerth Marathwada University. Scattered in 28 acres of land, the campus of the college renders students with a beautiful surroundings and pollution free ambiance to study.
"Shivlingeshwar College of Pharmacy prides itself on having the most experienced and knowledgeable teaching staff who are responsible for shaping the bright futures of tomorrow's pharmacists"
Dharashive opines, "At Shivlingeshwar College of Pharmacy, we strive to create a positive and vibrant environment for our students, which we consider will motivate them to participate freely in the class and learn better." The facilities that are encompassed in the campus includes strategically designed three storey college building, well-resourced and spacious laboratories, a magnificent library with latest edition books, journals and e-journals, lecture halls equipped with latest audio-visual gear, separate hostels for boys and girls and so on. The selection process of the students is however strictly governed by Government of Maharashtra through Director of Technical Education (DTE), which is purely based on the scoring of students in Maharashtra State - Common Entrance Test (MH-CET) and percentage of marks in subject of Physics, Chemistry and Biology/Mathematics in Higher secondary examination (HSC). Hence, as the entire selection process is monitored by DTE, best students are admitted irrespective of their economic and social status.
Emphasis on Practical Interventions
Shivlingeshwar College of Pharmacy prides itself on having the most experienced and knowledgeable teaching staff who are responsible for shaping the bright futures of tomorrow's pharmacists. "Selection of the bright candidate is not enough to create skilled and curious pharmacists; it's the teacher who ultimately guides the students to the path of perfection," pinpoints Dharashive. The students are taught using advanced and latest teaching techniques making sure that they are
clear with the concept at class and later are enable to apply those theories into practice. For the 3rd year students, the college even organizes one month industrial training where they get the opportunity to get a broad exposure to the industrial aspects of pharmacy. Dharashive shares, "Apart from industrial training we also invite experts from reputed industries for guest lectures in an attempt to fulfill the gap in the prescribed syllabus and make our students industry-ready."
Additionally, the college acknowledges a system called Continuous Student Improvement Program, aiming to constantly upgrade the students with new developments in the pharmacy industry. Under this program, various guest lectures and workshops for pharma field related subjects such as pharmacovigilance, clinical pharmacy, cGMP, research and development, regulatory and general training programs like personality development and communication skill improvement are conducted by the college regularly. The students also gain the scope to present their talents, in the college arranged National scientific event named "Vishwa-Pharma".
Existing for 14 years now, the institute has generated a number of pharmacy graduates and has strong alumni connections that are spread across the pharma sector holding leadership positions. "Ensuring that we don't lose contact with our past students, in every three years, the college conducts Alumni Meet Program that helps the present students to get industry insights from their senior and super-seniors. This strong bond also assists in facilitating the placement and industry-institute interaction activities," states Dharashive. Although optimum students prefer to pursue higher studies after completing their degrees at Shivlingeshwar College, the college takes initiatives for those who aspire to get placed through campus placement drive and invite companies like Tata Consultancy Services (TCS). In the upcoming years, the college targets to emerge as a center of excellence, providing holistic academic standards in Pharmacy.
Main Exams For Indian Students To Study Abroad
Aveek Pal Chaudhuri
USA vs UK vs CANADA: What's Best to Study Abroad?
Adarsh Khandelwal, Co-Founder, Collegify


Video Speed Controller is a free browser extension for the Google Chrome internet browser and suitable browsers that provides you higher video playback controls.
The extension appears to be associated to the Firefox extension Video Speed Controller which we reviewed earlier this yr.
Most video streaming websites supply primary playback controls solely; you may cease playback, skip to the following video, change the audio, and modify choose audio-visual settings. Some websites supply superior controls. YouTube, for instance, has a playback pace possibility within the settings menu that you could be activate to extend or lower playback pace.
Increasing is nice for audiobooks and every other content material that's largely audio-based, and if somebody talks actually quick in a video, reducing might assist sluggish all the pieces down a bit.
Fun Fact: Mini, the corporate that produces Mini Coopers, created Fast Forward for Chrome. A browser extension to speed up movies with the press of a button.
Video Speed Controller provides an overlay to HTML5 movies on supported websites. The extension features a blacklist of websites on which it doesn't work. While it ought to work on most video streaming websites, YouTube is an instance the place it really works completely on, it doesn't work on some streaming websites; the overlay is just not displayed on Dailymotion regardless that it's not blacklisted.
Increase or lower the playback pace of the video in .10 steps.
Skip 10 seconds forward or again.
Just choose any of the choices to activate the perform. Chrome customers preferring keyboard shortcuts over mouse controls could make use of the mapped ones as properly.
Tap on the keys to activate the perform whereas the video tab is lively. You needn't hover with the mouse cursor over the video for the shortcuts to work.
The extension's preferences give you choices to vary shortcuts and steps. You might change the rewind or advance step to 20 seconds, change the popular pace, or change the pace lower or improve steps.
Video Speed Controller helps two extra shortcuts that you could be map to keys. You might add "mute" or "pause" shortcuts to execute these with a key press as properly.
Hide the controller by default.
Blacklist websites to disable the extension when these websites are loaded.
Enable experimental options to disable site-specific keybindings.
Video Speed Controller is a helpful browser extension for Chrome and suitable browsers that provides customers management over the playback pace of HTML5 movies and choices to advance or return in movies with simply the press of the mouse or press of a key on the keyboard.