Answers.Body: <p><a href="https://en.wikipedia.org/wiki/Digital_forensics" rel="nofollow noreferrer">Digital Media Forensics</a> (DMF) field aims to develop technologies for the automated assessment of the integrity of an image or video, so <a href="https://farid.berkeley.edu/downloads/tutorials/digitalimageforensics.pdf" rel="nofollow noreferrer">DMF</a> is the field you are looking for. There are several approaches in DMF: for example, those based on machine learning (ML) techniques, in particular, convolutional neural networks (CNNs). </p>
  
  <p>For example, in the paper <a href="https://engineering.purdue.edu/~dgueraco/content/deepfake.pdf" rel="nofollow noreferrer">Deepfake Video Detection Using Recurrent Neural Networks</a> (2018), David Güera and Edward J. Delp propose a two-stage analysis composed of a CNN to extract features at the frame level followed by a temporally-aware RNN to capture temporal inconsistencies between frames introduced by the deepfake tool. More specifically, they use a convolutional LSTM architecture (CNN combined with an LSTM), which is trained end-to-end, so that the CNN learns the features in the videos, which are passed to the RNN, which attempts to predict the likelihood of those features belonging to a fake video or not. Section 3 explains the creation of deepfake videos, which leads to <em>inconsistencies between video frames</em> (which are exploited in the proposed method) because of the use of images with different viewing and illumination conditions.</p>
  
  <p>Other similar works have been proposed. See this curated list <a href="https://github.com/aerophile/awesome-deepfakes" rel="nofollow noreferrer">https://github.com/aerophile/awesome-deepfakes</a> for more related papers.</p>
  
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Answers.UserDisplayName: nbro
Answers.Body: <p>The techniques you mention use GANs. The key idea of GANs is that you have a generator and a discriminator. The generator generates new content, the discriminator has to tell if the content is from the real data or if it was generated.</p>
  
  <p>The discriminator is way more powerful. It should not be too hard to train a discriminator to detect fakes. Training a model which is able to pinpoint the manipulation and understanding of this is a proof of manipulation is harder. It is impossible to get a proof that something is not manipulated.</p>
  
  <p>About the question how you deal with photoshopped images: you look at differences in compression levels in the image. The keyword to look for is image forensics: <a href="http://fotoforensics.com/tutorial-estq.php" rel="nofollow noreferrer">http://fotoforensics.com/tutorial-estq.php</a></p>
  
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Answers.UserDisplayName: Martin Thoma
Answers.Body: <p>I think context is important here. Using tactics like those used by Scotland Yard for over a century is probably the best way. Establishing alibis, realistic time lines, motives. For a legal setting, it would be possible to prove these images were fake using methods like this. From an I.T. perspective, it may be possible to pinpoint an origin for these images. If thousands of duplicitous images came from a single origin, then any images from this origin are suspect.</p>
  
  <p>I think, in general, we should retrain ourselves to not believe everything we see. There are so many methods for faking images, that photography can no longer be considered to be the best evidence of an event occurring. We should not ignore all images, but instead seek outside concurrence of facts before jumping to conclusions. If all facts point to an event happening, then that photograph is likely to be real. </p>
  
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Answers.Body: <p>Assuming artifacts and unnatural elements do not exist in the media in question and that the media is indistinguishable to the human eye, the only way to be able to do this is to trace back to the source of the images. </p>
  
  <p>An analogy can be drawn to DoS (Denial of Service) attack, where an absurd number of requests are sent from a single IP to a single server causing it to crash - A common solution is a honeypot, where a high number of requests from one IP is redirected to a decoy server where, even if it crashes, uptime is not compromised. Some research has been done on these lines where <a href="https://www.tdcommons.org/cgi/viewcontent.cgi?article=2911&amp;context=dpubs_series" rel="nofollow noreferrer">this</a> paper spoke about verifying the digital signature of an image or <a href="https://ieeexplore.ieee.org/abstract/document/8755865" rel="nofollow noreferrer">this one</a> where they proposed tampered image detection and source camera identification. </p>
  
  <p>Once traced back to a source, if an absurd number of potentially fake images come from a singular source, it is to be questioned.</p>
  
  <p>The common fear arises when we are dealing with something, on the basis of the analogy, like a DDoS (Distributed Denial of Service) attack where each fake request comes from a distributed source - Network Security has found ways to deal with this, but security and fraud detection in the terms of AI just isn't that established.</p>
  
  <p>Essentially for a well thought out artificial media for a specific malicious purpose, today, is quite hard to be caught - But work is being done currently on security in AI. If you're planning on using artificial media for malicious purposes, I'd say now is the best time probably. </p>
  
  <p>This security has been a concern from a bit now. An <a href="https://towardsdatascience.com/from-faceapp-to-deepfakes-3d1048713da0" rel="nofollow noreferrer">article</a> written by a data scientist quotes </p>
  
  <blockquote>
    <p>Deepfakes have already been used to try to harass and humiliate women through fake porn videos. The term actually comes from the username of a Reddit user who was creating these videos by building generative adversarial networks (GANs) using TensorFlow. Now, intelligence officials are talking about the possibility of Vladimir Putin using fake videos to influence the 2020 presidential elections. More research is being done on deepfakes as a threat to democracy and national security, as well as how to detect them.</p>
  </blockquote>
  
  <p>Note - <em>I'm quite clueless about network security, all my knowledge comes from one conversation with a friend, and thought this would be a good analogy to use here. Forgive any errors in the analogy and please correct if possible!</em></p>
  
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Answers.Text: It would be nice if you could do some research and provide a link to at least 1 research work/paper which is based on something along those lines (that is, that exploits the source of the potentially fake videos).
Answers.UserDisplayName: nbro
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Answers.Text: Apart from [papers speaking about the potential harms](https://www.degruyter.com/downloadpdf/j/opis.2019.3.issue-1/opis-2019-0003/opis-2019-0003.pdf), and the ones commonly trying to detect artifacts, fewer papers doing whats stated in the answer such as [this one](https://www.tdcommons.org/cgi/viewcontent.cgi?article=2911&context=dpubs_series) or [this one](https://ieeexplore.ieee.org/abstract/document/8755865) - As said, extensive research has not been done on these lines, but it is being explored. Hope these links helped!
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Answers.UserDisplayName: ashenoy
Body: <p>With the growing ability to cheaply create fake pictures, fake soundbites, and fake video there becomes an increasing problem with recognizing what is real and what isn't. Even now we see a number of examples of applications that create fake media for little cost (see <a href="https://en.wikipedia.org/wiki/Deepfake" rel="noreferrer">Deepfake</a>, <a href="https://en.wikipedia.org/wiki/FaceApp" rel="noreferrer">FaceApp</a>, etc.).</p>
  
  <p>Obviously, if these applications are used in the wrong way they could be used to tarnish another person's image. Deepfake could be used to make a person look unfaithful to their partner. Another application could be used to make it seem like a politician said something controversial.</p>
  
  <p>What are some techniques that can be used to recognize and protect against artificially made media? </p>
  
Tags: <machine-learning><image-recognition><pattern-recognition><deepfakes>
Title: What are some tactics for recognizing artificially made media?
UserDisplayName: Andrew Butler
ThreadText: # What are some tactics for recognizing artificially made media?
  
  (Asked by: username_0 on 2018-04-07)
  
  With the growing ability to cheaply create fake pictures, fake soundbites, and fake video there becomes an increasing problem with recognizing what is real and what isn't. Even now we see a number of examples of applications that create fake media for little cost (see [Deepfake](https://en.wikipedia.org/wiki/Deepfake), [FaceApp](https://en.wikipedia.org/wiki/FaceApp), etc.).
  
  Obviously, if these applications are used in the wrong way they could be used to tarnish another person's image. Deepfake could be used to make a person look unfaithful to their partner. Another application could be used to make it seem like a politician said something controversial.
  
  What are some techniques that can be used to recognize and protect against artificially made media?
  
  **Tags:** machine-learning, image-recognition, pattern-recognition, deepfakes
  
  
  ---
  
  ## 4 answers
  
  ### Answer by username_1 on 2019-09-07. Score: 3
  
  [Digital Media Forensics](https://en.wikipedia.org/wiki/Digital_forensics) (DMF) field aims to develop technologies for the automated assessment of the integrity of an image or video, so [DMF](https://farid.berkeley.edu/downloads/tutorials/digitalimageforensics.pdf) is the field you are looking for. There are several approaches in DMF: for example, those based on machine learning (ML) techniques, in particular, convolutional neural networks (CNNs).
  
  For example, in the paper [Deepfake Video Detection Using Recurrent Neural Networks](https://engineering.purdue.edu/~dgueraco/content/deepfake.pdf) (2018), David Güera and Edward J. Delp propose a two-stage analysis composed of a CNN to extract features at the frame level followed by a temporally-aware RNN to capture temporal inconsistencies between frames introduced by the deepfake tool. More specifically, they use a convolutional LSTM architecture (CNN combined with an LSTM), which is trained end-to-end, so that the CNN learns the features in the videos, which are passed to the RNN, which attempts to predict the likelihood of those features belonging to a fake video or not. Section 3 explains the creation of deepfake videos, which leads to *inconsistencies between video frames* (which are exploited in the proposed method) because of the use of images with different viewing and illumination conditions.
  
  Other similar works have been proposed. See this curated list <https://github.com/aerophile/awesome-deepfakes> for more related papers.
  
  
  ---
  
  ### Answer by username_2 on 2018-11-12. Score: 1
  
  The techniques you mention use GANs. The key idea of GANs is that you have a generator and a discriminator. The generator generates new content, the discriminator has to tell if the content is from the real data or if it was generated.
  
  The discriminator is way more powerful. It should not be too hard to train a discriminator to detect fakes. Training a model which is able to pinpoint the manipulation and understanding of this is a proof of manipulation is harder. It is impossible to get a proof that something is not manipulated.
  
  About the question how you deal with photoshopped images: you look at differences in compression levels in the image. The keyword to look for is image forensics: <http://fotoforensics.com/tutorial-estq.php>
  
  
  ---
  
  ### Answer by username_3 on 2019-01-31. Score: 1
  
  I think context is important here. Using tactics like those used by Scotland Yard for over a century is probably the best way. Establishing alibis, realistic time lines, motives. For a legal setting, it would be possible to prove these images were fake using methods like this. From an I.T. perspective, it may be possible to pinpoint an origin for these images. If thousands of duplicitous images came from a single origin, then any images from this origin are suspect.
  
  I think, in general, we should retrain ourselves to not believe everything we see. There are so many methods for faking images, that photography can no longer be considered to be the best evidence of an event occurring. We should not ignore all images, but instead seek outside concurrence of facts before jumping to conclusions. If all facts point to an event happening, then that photograph is likely to be real.
  
  
  ---
  
  ### Answer by username_4 on 2019-08-08. Score: 0
  
  Assuming artifacts and unnatural elements do not exist in the media in question and that the media is indistinguishable to the human eye, the only way to be able to do this is to trace back to the source of the images.
  
  An analogy can be drawn to DoS (Denial of Service) attack, where an absurd number of requests are sent from a single IP to a single server causing it to crash - A common solution is a honeypot, where a high number of requests from one IP is redirected to a decoy server where, even if it crashes, uptime is not compromised. Some research has been done on these lines where [this](https://www.tdcommons.org/cgi/viewcontent.cgi?article=2911&context=dpubs_series) paper spoke about verifying the digital signature of an image or [this one](https://ieeexplore.ieee.org/abstract/document/8755865) where they proposed tampered image detection and source camera identification.
  
  Once traced back to a source, if an absurd number of potentially fake images come from a singular source, it is to be questioned.
  
  The common fear arises when we are dealing with something, on the basis of the analogy, like a DDoS (Distributed Denial of Service) attack where each fake request comes from a distributed source - Network Security has found ways to deal with this, but security and fraud detection in the terms of AI just isn't that established.
  
  Essentially for a well thought out artificial media for a specific malicious purpose, today, is quite hard to be caught - But work is being done currently on security in AI. If you're planning on using artificial media for malicious purposes, I'd say now is the best time probably.
  
  This security has been a concern from a bit now. An [article](https://towardsdatascience.com/from-faceapp-to-deepfakes-3d1048713da0) written by a data scientist quotes
  
  > Deepfakes have already been used to try to harass and humiliate women through fake porn videos. The term actually comes from the username of a Reddit user who was creating these videos by building generative adversarial networks (GANs) using TensorFlow. Now, intelligence officials are talking about the possibility of Vladimir Putin using fake videos to influence the 2020 presidential elections. More research is being done on deepfakes as a threat to democracy and national security, as well as how to detect them.
  
  Note - *I'm quite clueless about network security, all my knowledge comes from one conversation with a friend, and thought this would be a good analogy to use here. Forgive any errors in the analogy and please correct if possible!*
  
  > **Comments:**
  > **username_1**: It would be nice if you could do some research and provide a link to at least 1 research work/paper which is based on something along those lines (that is, that exploits the source of the potentially fake videos).  
  > **username_4**: Apart from [papers speaking about the potential harms](https://www.degruyter.com/downloadpdf/j/opis.2019.3.issue-1/opis-2019-0003/opis-2019-0003.pdf), and the ones commonly trying to detect artifacts, fewer papers doing whats stated in the answer such as [this one](https://www.tdcommons.org/cgi/viewcontent.cgi?article=2911&context=dpubs_series) or [this one](https://ieeexplore.ieee.org/abstract/document/8755865) - As said, extensive research has not been done on these lines, but it is being explored. Hope these links helped!  
  
  ---
  
  