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Datasets:
lens-ai
/
adversarial_pcam

Tasks:
Image Classification
Modalities:
Image
Formats:
imagefolder
Size:
< 1K
Tags:
adversarial
image-classification
robustness
deep-learning
computer-vision
Libraries:
Datasets
Dataset card Data Studio Files Community
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  1. README.md +80 -50
  2. dataset.json +662 -364
  3. perturbations/perturbation_0_Boundary_Attack.png +0 -0
  4. perturbations/perturbation_0_Enhanced_Square_Attack.png +0 -0
  5. perturbations/perturbation_0_HopSkipJump_Attack.png +0 -0
  6. perturbations/perturbation_0_SimBA.png +0 -0
  7. perturbations/perturbation_0_SpatialTransformation_Attack.png +0 -0
  8. perturbations/perturbation_0_Zoo_Attack.png +0 -0
  9. perturbations/perturbation_1_Boundary_Attack.png +0 -0
  10. perturbations/perturbation_1_Enhanced_Square_Attack.png +0 -0
  11. perturbations/perturbation_1_HopSkipJump_Attack.png +0 -0
  12. perturbations/perturbation_1_SimBA.png +0 -0
  13. perturbations/perturbation_1_SpatialTransformation_Attack.png +0 -0
  14. perturbations/perturbation_1_Zoo_Attack.png +0 -0
  15. perturbations/perturbation_2_Boundary_Attack.png +0 -0
  16. perturbations/perturbation_2_Enhanced_Square_Attack.png +0 -0
  17. perturbations/perturbation_2_HopSkipJump_Attack.png +0 -0
  18. perturbations/perturbation_2_SimBA.png +0 -0
  19. perturbations/perturbation_2_SpatialTransformation_Attack.png +0 -0
  20. perturbations/perturbation_2_Zoo_Attack.png +0 -0
  21. perturbations/perturbation_3_Boundary_Attack.png +0 -0
  22. perturbations/perturbation_3_Enhanced_Square_Attack.png +0 -0
  23. perturbations/perturbation_3_HopSkipJump_Attack.png +0 -0
  24. perturbations/perturbation_3_SimBA.png +0 -0
  25. perturbations/perturbation_3_SpatialTransformation_Attack.png +0 -0
  26. perturbations/perturbation_3_Zoo_Attack.png +0 -0
  27. perturbations/perturbation_4_Boundary_Attack.png +0 -0
  28. perturbations/perturbation_4_Enhanced_Square_Attack.png +0 -0
  29. perturbations/perturbation_4_HopSkipJump_Attack.png +0 -0
  30. perturbations/perturbation_4_SimBA.png +0 -0
  31. perturbations/perturbation_4_SpatialTransformation_Attack.png +0 -0
  32. perturbations/perturbation_4_Zoo_Attack.png +0 -0
  33. perturbations/perturbation_5_Boundary_Attack.png +0 -0
  34. perturbations/perturbation_5_Enhanced_Square_Attack.png +0 -0
  35. perturbations/perturbation_5_HopSkipJump_Attack.png +0 -0
  36. perturbations/perturbation_5_SimBA.png +0 -0
  37. perturbations/perturbation_5_SpatialTransformation_Attack.png +0 -0
  38. perturbations/perturbation_5_Zoo_Attack.png +0 -0
  39. perturbations/perturbation_6_Boundary_Attack.png +0 -0
  40. perturbations/perturbation_6_Enhanced_Square_Attack.png +0 -0
  41. perturbations/perturbation_6_HopSkipJump_Attack.png +0 -0
  42. perturbations/perturbation_6_SimBA.png +0 -0
  43. perturbations/perturbation_6_SpatialTransformation_Attack.png +0 -0
  44. perturbations/perturbation_6_Zoo_Attack.png +0 -0
  45. perturbations/perturbation_7_Boundary_Attack.png +0 -0
  46. perturbations/perturbation_7_Enhanced_Square_Attack.png +0 -0
  47. perturbations/perturbation_7_HopSkipJump_Attack.png +0 -0
  48. perturbations/perturbation_7_SimBA.png +0 -0
  49. perturbations/perturbation_7_SpatialTransformation_Attack.png +0 -0
  50. perturbations/perturbation_7_Zoo_Attack.png +0 -0
README.md CHANGED
@@ -1,4 +1,16 @@
1
  ---
 
 
 
 
 
 
 
 
 
 
 
 
2
  tags:
3
  - adversarial
4
  - image-classification
@@ -16,80 +28,101 @@ This dataset contains adversarial examples generated using various attack techni
16
  **[lens-ai/clip-vit-base-patch32_pcam_finetuned](https://huggingface.co/lens-ai/clip-vit-base-patch32_pcam_finetuned)**.
17
 
18
  Researchers and engineers can use this dataset to:
19
- - Evaluate model robustness against adversarial attacks.
20
- - Train models with adversarial data for improved resilience.
21
- - Benchmark new adversarial defense mechanisms.
22
 
23
  ---
24
 
25
  ## **📂 Dataset Structure**
26
- adv_datasets/ │── black_box_attacks/ │ │── HopSkipJump/ │ │ ├── adv_0_labelfalse.png │ │ ├── adv_0_labelfalse_pred1.png │ │ ├── orig_0_labelfalse.png │ │ ├── perturbation_0.png │ │── Square_Attack/ │ │── SimBA/ │ │── Zoo_Attack/ │── non_black_box_attacks/ │ │── Boundary_Attack/ │ │── Spatial_Transformation_Attack/ │ │── Pixel_Attack/ │── attack_success_rates.json # Metrics for each attack on your fine-tuned model │── metadata.json # Dataset metadata │── README.md # Hugging Face Dataset Description
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
27
 
 
 
 
 
 
28
 
29
- Each adversarial image is stored in a structured format:
30
- - `adv_{id}_labelfalse.png` → **Adversarial image** generated for a given input.
31
- - `adv_{id}_labelfalse_pred{pred_label}.png` → **Adversarial image with model prediction.**
32
- - `orig_{id}_*.png` → **Original image** before perturbation.
33
- - `perturbation_{id}.png` → **The perturbation applied to the original image.**
34
- - **Subdirectories represent attack methods** (black-box vs. non-black-box).
 
 
 
 
 
 
35
 
36
  ---
37
 
38
- ## **🔹 Attack Types Explained**
39
- The dataset contains **black-box** and **non-black-box** adversarial attacks.
40
 
41
- ### **1️⃣ Black-Box Attacks (No access to model gradients)**
42
- These attacks do **not** require knowledge of the model’s internal structure.
43
 
44
  #### **🔹 HopSkipJump Attack**
45
- - A query-efficient black-box attack that estimates gradients and generates adversarial samples.
46
- - Based on **decision boundary approximation**.
47
-
48
- #### **🔹 Square Attack**
49
- - A score-based black-box attack that applies **random pixel perturbations**.
50
- - Works well in high-dimensional spaces.
51
-
52
- #### **🔹 SimBA (Simple Black-box Attack)**
53
- - Uses **random perturbations** to mislead the model while reducing query complexity.
54
 
55
  #### **🔹 Zoo Attack**
56
- - A zeroth-order optimization (ZOO) attack that estimates gradients via **finite-difference methods**.
 
57
 
58
- ---
 
59
 
60
- ### **2️⃣ Non-Black-Box Attacks (Requires model gradients)**
61
- These attacks assume **access to the model's parameters and gradients**.
 
62
 
63
  #### **🔹 Boundary Attack**
64
- - A query-efficient attack that **moves samples along the decision boundary** with minimal perturbation.
 
65
 
66
  #### **🔹 Spatial Transformation Attack**
67
- - Uses **rotation, scaling, and translation** to deceive the model without pixel-level perturbations.
68
-
69
- #### **🔹 Pixel Attack**
70
- - Modifies only a **few pixels** in the image to change the model’s prediction.
71
 
72
  ---
73
 
74
- ## Usage of the Dataset
75
 
76
  ```python
77
-
78
  from datasets import load_dataset
79
  import torch
80
  from torchvision import transforms
81
  from PIL import Image
82
 
83
- # Load the dataset from Hugging Face
84
- dataset = load_dataset("lens-ai/adversarial_pcam")
85
 
86
- # Define transformation for training
87
  transform = transforms.Compose([
88
  transforms.Resize((224, 224)),
89
  transforms.ToTensor()
90
  ])
91
 
92
- # Load an image and apply transformations
93
  def process_sample(example):
94
  img = Image.open(example["image"]).convert("RGB")
95
  return {"image": transform(img), "label": example["label"]}
@@ -103,29 +136,26 @@ label = dataset["train"][0]["label"]
103
 
104
  print("Label:", label)
105
  print("Image shape:", img_tensor.shape) # Should be (3, 224, 224)
106
-
107
  ```
108
 
109
-
110
  ## **📊 Attack Success Rates**
111
- The dataset includes `attack_success_rates.json`, which provides:
112
- - **Success rates for each attack** on `clip-vit-base-patch32_pcam_finetuned`.
113
- - Average confidence scores before and after attack.
114
-
115
- Example:
116
  ```json
117
  {
118
  "HopSkipJump": {"success_rate": 14},
119
- "Square_Attack": {"success_rate": 22}
120
- "Boundary_Attack": {"success_rate": 98}
121
- "SimBA": {"success_rate": 99}
122
- "SpatialTransformation_Attack": 99}
123
  }
 
124
 
 
 
125
  @article{lensai2025adversarial,
126
  title={Adversarial PCAM Dataset},
127
  author={LensAI Team},
128
  year={2025},
129
  url={https://huggingface.co/datasets/lens-ai/adversarial_pcam}
130
  }
131
-
 
1
  ---
2
+ tags:
3
+ - adversarial
4
+ - image-classification
5
+ - robustness
6
+ - deep-learning
7
+ - computer-vision
8
+ task_categories:
9
+ - image-classification
10
+ model:
11
+ - lens-ai/clip-vit-base-patch32_pcam_finetuned
12
+ ---
13
+ ---
14
  tags:
15
  - adversarial
16
  - image-classification
 
28
  **[lens-ai/clip-vit-base-patch32_pcam_finetuned](https://huggingface.co/lens-ai/clip-vit-base-patch32_pcam_finetuned)**.
29
 
30
  Researchers and engineers can use this dataset to:
31
+ - Evaluate model robustness against adversarial attacks
32
+ - Train models with adversarial data for improved resilience
33
+ - Benchmark new adversarial defense mechanisms
34
 
35
  ---
36
 
37
  ## **📂 Dataset Structure**
38
+ ```
39
+ organized_dataset/
40
+ ├── train/
41
+ │ ├── 0/ # Negative samples
42
+ │ │ ├── adv_0_labelfalse_pred1_SquareAttack.png
43
+ │ │ ├── orig_0_labelfalse_SquareAttack.png
44
+ │ │ └── ...
45
+ │ └── 1/ # Positive samples
46
+ │ ├── adv_1_labeltrue_pred0_SquareAttack.png
47
+ │ ├── orig_1_labeltrue_SquareAttack.png
48
+ │ └── ...
49
+ ├── perturbations/
50
+ │ ├── perturbation_0_SquareAttack.png
51
+ │ ├── perturbation_1_SquareAttack.png
52
+ │ └── ...
53
+ └── dataset.json
54
+ ```
55
 
56
+ Each adversarial example consists of:
57
+ - `adv_{id}_label{true/false}_pred{pred_label}_{attack_name}.png` → **Adversarial image** with model prediction
58
+ - `orig_{id}_label{true/false}_{attack_name}.png` → **Original image** before perturbation
59
+ - `perturbation_{id}_{attack_name}.png` → **The perturbation applied** to the original image
60
+ - **Attack name in filename** indicates which method was used
61
 
62
+ The `dataset.json` file contains detailed metadata for each sample, including:
63
+ ```json
64
+ {
65
+ "attack": "SquareAttack",
66
+ "type": "black_box_attacks",
67
+ "perturbation": "perturbations/perturbation_1_SquareAttack.png",
68
+ "adversarial": "train/0/adv_1_labelfalse_pred1_SquareAttack.png",
69
+ "original": ["train/0/orig_1_labelfalse_SquareAttack.png"],
70
+ "label": 0,
71
+ "prediction": 1
72
+ }
73
+ ```
74
 
75
  ---
76
 
77
+ ## **🔹 Attack Types**
78
+ The dataset contains both black-box and non-black-box adversarial attacks.
79
 
80
+ ### **1️⃣ Black-Box Attacks**
81
+ These attacks do not require access to model gradients:
82
 
83
  #### **🔹 HopSkipJump Attack**
84
+ - Query-efficient black-box attack that estimates gradients
85
+ - Based on decision boundary approximation
 
 
 
 
 
 
 
86
 
87
  #### **🔹 Zoo Attack**
88
+ - Zeroth-order optimization (ZOO) attack
89
+ - Estimates gradients via finite-difference methods
90
 
91
+ ### **2️⃣ Non-Black-Box Attacks**
92
+ These attacks require access to model gradients:
93
 
94
+ #### **🔹 SimBA (Simple Black-box Attack)**
95
+ - Uses random perturbations to mislead the model
96
+ - Reduces query complexity
97
 
98
  #### **🔹 Boundary Attack**
99
+ - Query-efficient attack moving along decision boundary
100
+ - Minimizes perturbation size
101
 
102
  #### **🔹 Spatial Transformation Attack**
103
+ - Uses rotation, scaling, and translation
104
+ - No pixel-level perturbations required
 
 
105
 
106
  ---
107
 
108
+ ## Usage
109
 
110
  ```python
 
111
  from datasets import load_dataset
112
  import torch
113
  from torchvision import transforms
114
  from PIL import Image
115
 
116
+ # Load the dataset
117
+ dataset = load_dataset("your-org/adversarial_pcam")
118
 
119
+ # Define transformation
120
  transform = transforms.Compose([
121
  transforms.Resize((224, 224)),
122
  transforms.ToTensor()
123
  ])
124
 
125
+ # Process samples
126
  def process_sample(example):
127
  img = Image.open(example["image"]).convert("RGB")
128
  return {"image": transform(img), "label": example["label"]}
 
136
 
137
  print("Label:", label)
138
  print("Image shape:", img_tensor.shape) # Should be (3, 224, 224)
 
139
  ```
140
 
 
141
  ## **📊 Attack Success Rates**
142
+ Success rates for each attack on the target model:
 
 
 
 
143
  ```json
144
  {
145
  "HopSkipJump": {"success_rate": 14},
146
+ "Zoo_Attack": {"success_rate": 22},
147
+ "SimBA": {"success_rate": 99},
148
+ "Boundary_Attack": {"success_rate": 98},
149
+ "SpatialTransformation_Attack": {"success_rate": 99}
150
  }
151
+ ```
152
 
153
+ ## Citation
154
+ ```bibtex
155
  @article{lensai2025adversarial,
156
  title={Adversarial PCAM Dataset},
157
  author={LensAI Team},
158
  year={2025},
159
  url={https://huggingface.co/datasets/lens-ai/adversarial_pcam}
160
  }
161
+ ```
dataset.json CHANGED
@@ -1,364 +1,662 @@
1
- {
2
- "train": [
3
- {
4
- "attack": "HopSkipJump_Attack",
5
- "type": "black_box",
6
- "perturbation": "black_box_attacks/HopSkipJump_Attack/perturbation_2.png",
7
- "adversarial": "black_box_attacks/HopSkipJump_Attack/adv_2_labelFalse.png",
8
- "original": [
9
- "black_box_attacks/HopSkipJump_Attack/orig_2_labelFalse.png"
10
- ]
11
- },
12
- {
13
- "attack": "HopSkipJump_Attack",
14
- "type": "black_box",
15
- "perturbation": "black_box_attacks/HopSkipJump_Attack/perturbation_3.png",
16
- "adversarial": "black_box_attacks/HopSkipJump_Attack/adv_3_labelFalse.png",
17
- "original": [
18
- "black_box_attacks/HopSkipJump_Attack/orig_3_labelFalse.png"
19
- ]
20
- },
21
- {
22
- "attack": "HopSkipJump_Attack",
23
- "type": "black_box",
24
- "perturbation": "black_box_attacks/HopSkipJump_Attack/perturbation_1.png",
25
- "adversarial": "black_box_attacks/HopSkipJump_Attack/adv_1_labelFalse.png",
26
- "original": [
27
- "black_box_attacks/HopSkipJump_Attack/orig_1_labelFalse.png"
28
- ]
29
- },
30
- {
31
- "attack": "HopSkipJump_Attack",
32
- "type": "black_box",
33
- "perturbation": "black_box_attacks/HopSkipJump_Attack/perturbation_0.png",
34
- "adversarial": "black_box_attacks/HopSkipJump_Attack/adv_0_labelFalse.png",
35
- "original": [
36
- "black_box_attacks/HopSkipJump_Attack/orig_0_labelFalse.png"
37
- ]
38
- },
39
- {
40
- "attack": "HopSkipJump_Attack",
41
- "type": "black_box",
42
- "perturbation": "black_box_attacks/HopSkipJump_Attack/perturbation_4.png",
43
- "adversarial": "black_box_attacks/HopSkipJump_Attack/adv_4_labelFalse.png",
44
- "original": [
45
- "black_box_attacks/HopSkipJump_Attack/orig_4_labelFalse.png"
46
- ]
47
- },
48
- {
49
- "attack": "HopSkipJump_Attack",
50
- "type": "black_box",
51
- "perturbation": "black_box_attacks/HopSkipJump_Attack/perturbation_5.png",
52
- "adversarial": "black_box_attacks/HopSkipJump_Attack/adv_5_labelTrue.png",
53
- "original": [
54
- "black_box_attacks/HopSkipJump_Attack/orig_5_labelTrue.png"
55
- ]
56
- },
57
- {
58
- "attack": "HopSkipJump_Attack",
59
- "type": "black_box",
60
- "perturbation": "black_box_attacks/HopSkipJump_Attack/perturbation_7.png",
61
- "adversarial": "black_box_attacks/HopSkipJump_Attack/adv_7_labelFalse.png",
62
- "original": [
63
- "black_box_attacks/HopSkipJump_Attack/orig_7_labelFalse.png"
64
- ]
65
- },
66
- {
67
- "attack": "HopSkipJump_Attack",
68
- "type": "black_box",
69
- "perturbation": "black_box_attacks/HopSkipJump_Attack/perturbation_6.png",
70
- "adversarial": "black_box_attacks/HopSkipJump_Attack/adv_6_labelTrue.png",
71
- "original": [
72
- "black_box_attacks/HopSkipJump_Attack/orig_6_labelTrue.png"
73
- ]
74
- },
75
- {
76
- "attack": "HopSkipJump_Attack",
77
- "type": "black_box",
78
- "perturbation": "black_box_attacks/HopSkipJump_Attack/perturbation_8.png",
79
- "adversarial": "black_box_attacks/HopSkipJump_Attack/adv_8_labelFalse.png",
80
- "original": [
81
- "black_box_attacks/HopSkipJump_Attack/orig_8_labelFalse.png"
82
- ]
83
- },
84
- {
85
- "attack": "HopSkipJump_Attack",
86
- "type": "black_box",
87
- "perturbation": "black_box_attacks/HopSkipJump_Attack/perturbation_9.png",
88
- "adversarial": "black_box_attacks/HopSkipJump_Attack/adv_9_labelFalse.png",
89
- "original": [
90
- "black_box_attacks/HopSkipJump_Attack/orig_9_labelFalse.png"
91
- ]
92
- },
93
- {
94
- "attack": "Enhanced_Square_Attack",
95
- "type": "black_box",
96
- "perturbation": "black_box_attacks/Enhanced_Square_Attack/perturbation_2.png",
97
- "adversarial": "black_box_attacks/Enhanced_Square_Attack/adv_2_labelFalse.png",
98
- "original": [
99
- "black_box_attacks/Enhanced_Square_Attack/orig_2_labelFalse.png"
100
- ]
101
- },
102
- {
103
- "attack": "Enhanced_Square_Attack",
104
- "type": "black_box",
105
- "perturbation": "black_box_attacks/Enhanced_Square_Attack/perturbation_3.png",
106
- "adversarial": "black_box_attacks/Enhanced_Square_Attack/adv_3_labelFalse.png",
107
- "original": [
108
- "black_box_attacks/Enhanced_Square_Attack/orig_3_labelFalse.png"
109
- ]
110
- },
111
- {
112
- "attack": "Enhanced_Square_Attack",
113
- "type": "black_box",
114
- "perturbation": "black_box_attacks/Enhanced_Square_Attack/perturbation_1.png",
115
- "adversarial": "black_box_attacks/Enhanced_Square_Attack/adv_1_labelFalse.png",
116
- "original": [
117
- "black_box_attacks/Enhanced_Square_Attack/orig_1_labelFalse.png"
118
- ]
119
- },
120
- {
121
- "attack": "Enhanced_Square_Attack",
122
- "type": "black_box",
123
- "perturbation": "black_box_attacks/Enhanced_Square_Attack/perturbation_0.png",
124
- "adversarial": "black_box_attacks/Enhanced_Square_Attack/adv_0_labelFalse.png",
125
- "original": [
126
- "black_box_attacks/Enhanced_Square_Attack/orig_0_labelFalse.png"
127
- ]
128
- },
129
- {
130
- "attack": "Enhanced_Square_Attack",
131
- "type": "black_box",
132
- "perturbation": "black_box_attacks/Enhanced_Square_Attack/perturbation_4.png",
133
- "adversarial": "black_box_attacks/Enhanced_Square_Attack/adv_4_labelFalse.png",
134
- "original": [
135
- "black_box_attacks/Enhanced_Square_Attack/orig_4_labelFalse.png"
136
- ]
137
- },
138
- {
139
- "attack": "Enhanced_Square_Attack",
140
- "type": "black_box",
141
- "perturbation": "black_box_attacks/Enhanced_Square_Attack/perturbation_5.png",
142
- "adversarial": "black_box_attacks/Enhanced_Square_Attack/adv_5_labelTrue.png",
143
- "original": [
144
- "black_box_attacks/Enhanced_Square_Attack/orig_5_labelTrue.png"
145
- ]
146
- },
147
- {
148
- "attack": "Enhanced_Square_Attack",
149
- "type": "black_box",
150
- "perturbation": "black_box_attacks/Enhanced_Square_Attack/perturbation_7.png",
151
- "adversarial": "black_box_attacks/Enhanced_Square_Attack/adv_7_labelFalse.png",
152
- "original": [
153
- "black_box_attacks/Enhanced_Square_Attack/orig_7_labelFalse.png"
154
- ]
155
- },
156
- {
157
- "attack": "Enhanced_Square_Attack",
158
- "type": "black_box",
159
- "perturbation": "black_box_attacks/Enhanced_Square_Attack/perturbation_6.png",
160
- "adversarial": "black_box_attacks/Enhanced_Square_Attack/adv_6_labelTrue.png",
161
- "original": [
162
- "black_box_attacks/Enhanced_Square_Attack/orig_6_labelTrue.png"
163
- ]
164
- },
165
- {
166
- "attack": "Enhanced_Square_Attack",
167
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perturbations/perturbation_0_Enhanced_Square_Attack.png ADDED
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perturbations/perturbation_0_SimBA.png ADDED
perturbations/perturbation_0_SpatialTransformation_Attack.png ADDED
perturbations/perturbation_0_Zoo_Attack.png ADDED
perturbations/perturbation_1_Boundary_Attack.png ADDED
perturbations/perturbation_1_Enhanced_Square_Attack.png ADDED
perturbations/perturbation_1_HopSkipJump_Attack.png ADDED
perturbations/perturbation_1_SimBA.png ADDED
perturbations/perturbation_1_SpatialTransformation_Attack.png ADDED
perturbations/perturbation_1_Zoo_Attack.png ADDED
perturbations/perturbation_2_Boundary_Attack.png ADDED
perturbations/perturbation_2_Enhanced_Square_Attack.png ADDED
perturbations/perturbation_2_HopSkipJump_Attack.png ADDED
perturbations/perturbation_2_SimBA.png ADDED
perturbations/perturbation_2_SpatialTransformation_Attack.png ADDED
perturbations/perturbation_2_Zoo_Attack.png ADDED
perturbations/perturbation_3_Boundary_Attack.png ADDED
perturbations/perturbation_3_Enhanced_Square_Attack.png ADDED
perturbations/perturbation_3_HopSkipJump_Attack.png ADDED
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perturbations/perturbation_3_SpatialTransformation_Attack.png ADDED
perturbations/perturbation_3_Zoo_Attack.png ADDED
perturbations/perturbation_4_Boundary_Attack.png ADDED
perturbations/perturbation_4_Enhanced_Square_Attack.png ADDED
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perturbations/perturbation_4_Zoo_Attack.png ADDED
perturbations/perturbation_5_Boundary_Attack.png ADDED
perturbations/perturbation_5_Enhanced_Square_Attack.png ADDED
perturbations/perturbation_5_HopSkipJump_Attack.png ADDED
perturbations/perturbation_5_SimBA.png ADDED
perturbations/perturbation_5_SpatialTransformation_Attack.png ADDED
perturbations/perturbation_5_Zoo_Attack.png ADDED
perturbations/perturbation_6_Boundary_Attack.png ADDED
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perturbations/perturbation_6_SpatialTransformation_Attack.png ADDED
perturbations/perturbation_6_Zoo_Attack.png ADDED
perturbations/perturbation_7_Boundary_Attack.png ADDED
perturbations/perturbation_7_Enhanced_Square_Attack.png ADDED
perturbations/perturbation_7_HopSkipJump_Attack.png ADDED
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perturbations/perturbation_7_SpatialTransformation_Attack.png ADDED
perturbations/perturbation_7_Zoo_Attack.png ADDED