Datasets:
Venkata Pydipalli
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- README.md +80 -50
- dataset.json +662 -364
- perturbations/perturbation_0_Boundary_Attack.png +0 -0
- perturbations/perturbation_0_Enhanced_Square_Attack.png +0 -0
- perturbations/perturbation_0_HopSkipJump_Attack.png +0 -0
- perturbations/perturbation_0_SimBA.png +0 -0
- perturbations/perturbation_0_SpatialTransformation_Attack.png +0 -0
- perturbations/perturbation_0_Zoo_Attack.png +0 -0
- perturbations/perturbation_1_Boundary_Attack.png +0 -0
- perturbations/perturbation_1_Enhanced_Square_Attack.png +0 -0
- perturbations/perturbation_1_HopSkipJump_Attack.png +0 -0
- perturbations/perturbation_1_SimBA.png +0 -0
- perturbations/perturbation_1_SpatialTransformation_Attack.png +0 -0
- perturbations/perturbation_1_Zoo_Attack.png +0 -0
- perturbations/perturbation_2_Boundary_Attack.png +0 -0
- perturbations/perturbation_2_Enhanced_Square_Attack.png +0 -0
- perturbations/perturbation_2_HopSkipJump_Attack.png +0 -0
- perturbations/perturbation_2_SimBA.png +0 -0
- perturbations/perturbation_2_SpatialTransformation_Attack.png +0 -0
- perturbations/perturbation_2_Zoo_Attack.png +0 -0
- perturbations/perturbation_3_Boundary_Attack.png +0 -0
- perturbations/perturbation_3_Enhanced_Square_Attack.png +0 -0
- perturbations/perturbation_3_HopSkipJump_Attack.png +0 -0
- perturbations/perturbation_3_SimBA.png +0 -0
- perturbations/perturbation_3_SpatialTransformation_Attack.png +0 -0
- perturbations/perturbation_3_Zoo_Attack.png +0 -0
- perturbations/perturbation_4_Boundary_Attack.png +0 -0
- perturbations/perturbation_4_Enhanced_Square_Attack.png +0 -0
- perturbations/perturbation_4_HopSkipJump_Attack.png +0 -0
- perturbations/perturbation_4_SimBA.png +0 -0
- perturbations/perturbation_4_SpatialTransformation_Attack.png +0 -0
- perturbations/perturbation_4_Zoo_Attack.png +0 -0
- perturbations/perturbation_5_Boundary_Attack.png +0 -0
- perturbations/perturbation_5_Enhanced_Square_Attack.png +0 -0
- perturbations/perturbation_5_HopSkipJump_Attack.png +0 -0
- perturbations/perturbation_5_SimBA.png +0 -0
- perturbations/perturbation_5_SpatialTransformation_Attack.png +0 -0
- perturbations/perturbation_5_Zoo_Attack.png +0 -0
- perturbations/perturbation_6_Boundary_Attack.png +0 -0
- perturbations/perturbation_6_Enhanced_Square_Attack.png +0 -0
- perturbations/perturbation_6_HopSkipJump_Attack.png +0 -0
- perturbations/perturbation_6_SimBA.png +0 -0
- perturbations/perturbation_6_SpatialTransformation_Attack.png +0 -0
- perturbations/perturbation_6_Zoo_Attack.png +0 -0
- perturbations/perturbation_7_Boundary_Attack.png +0 -0
- perturbations/perturbation_7_Enhanced_Square_Attack.png +0 -0
- perturbations/perturbation_7_HopSkipJump_Attack.png +0 -0
- perturbations/perturbation_7_SimBA.png +0 -0
- perturbations/perturbation_7_SpatialTransformation_Attack.png +0 -0
- perturbations/perturbation_7_Zoo_Attack.png +0 -0
README.md
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---
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tags:
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- adversarial
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- image-classification
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**[lens-ai/clip-vit-base-patch32_pcam_finetuned](https://huggingface.co/lens-ai/clip-vit-base-patch32_pcam_finetuned)**.
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Researchers and engineers can use this dataset to:
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- Evaluate model robustness against adversarial attacks
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- Train models with adversarial data for improved resilience
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- Benchmark new adversarial defense mechanisms
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---
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## **📂 Dataset Structure**
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---
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## **🔹 Attack Types
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The dataset contains
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### **1️⃣ Black-Box Attacks
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These attacks do
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#### **🔹 HopSkipJump Attack**
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- Based on
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#### **🔹 Square Attack**
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- A score-based black-box attack that applies **random pixel perturbations**.
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- Works well in high-dimensional spaces.
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#### **🔹 SimBA (Simple Black-box Attack)**
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- Uses **random perturbations** to mislead the model while reducing query complexity.
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#### **🔹 Zoo Attack**
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#### **🔹 Boundary Attack**
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#### **🔹 Spatial Transformation Attack**
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- Uses
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#### **🔹 Pixel Attack**
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- Modifies only a **few pixels** in the image to change the model’s prediction.
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---
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## Usage
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```python
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from datasets import load_dataset
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import torch
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from torchvision import transforms
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from PIL import Image
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# Load the dataset
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dataset = load_dataset("
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# Define transformation
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transform = transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.ToTensor()
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])
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#
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def process_sample(example):
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img = Image.open(example["image"]).convert("RGB")
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return {"image": transform(img), "label": example["label"]}
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print("Label:", label)
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print("Image shape:", img_tensor.shape) # Should be (3, 224, 224)
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```
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## **📊 Attack Success Rates**
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- **Success rates for each attack** on `clip-vit-base-patch32_pcam_finetuned`.
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- Average confidence scores before and after attack.
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Example:
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```json
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{
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"HopSkipJump": {"success_rate": 14},
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"
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"SpatialTransformation_Attack": 99}
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}
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@article{lensai2025adversarial,
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title={Adversarial PCAM Dataset},
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author={LensAI Team},
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year={2025},
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url={https://huggingface.co/datasets/lens-ai/adversarial_pcam}
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}
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-
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---
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tags:
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- adversarial
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- image-classification
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- robustness
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- deep-learning
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- computer-vision
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task_categories:
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- image-classification
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model:
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- lens-ai/clip-vit-base-patch32_pcam_finetuned
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---
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---
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tags:
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- adversarial
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- image-classification
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**[lens-ai/clip-vit-base-patch32_pcam_finetuned](https://huggingface.co/lens-ai/clip-vit-base-patch32_pcam_finetuned)**.
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Researchers and engineers can use this dataset to:
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- Evaluate model robustness against adversarial attacks
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- Train models with adversarial data for improved resilience
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- Benchmark new adversarial defense mechanisms
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---
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## **📂 Dataset Structure**
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```
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organized_dataset/
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├── train/
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│ ├── 0/ # Negative samples
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│ │ ├── adv_0_labelfalse_pred1_SquareAttack.png
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│ │ ├── orig_0_labelfalse_SquareAttack.png
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│ │ └── ...
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│ └── 1/ # Positive samples
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│ ├── adv_1_labeltrue_pred0_SquareAttack.png
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│ ├── orig_1_labeltrue_SquareAttack.png
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│ └── ...
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├── perturbations/
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│ ├── perturbation_0_SquareAttack.png
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│ ├── perturbation_1_SquareAttack.png
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│ └── ...
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└── dataset.json
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```
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Each adversarial example consists of:
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- `adv_{id}_label{true/false}_pred{pred_label}_{attack_name}.png` → **Adversarial image** with model prediction
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- `orig_{id}_label{true/false}_{attack_name}.png` → **Original image** before perturbation
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- `perturbation_{id}_{attack_name}.png` → **The perturbation applied** to the original image
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- **Attack name in filename** indicates which method was used
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The `dataset.json` file contains detailed metadata for each sample, including:
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```json
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{
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"attack": "SquareAttack",
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"type": "black_box_attacks",
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"perturbation": "perturbations/perturbation_1_SquareAttack.png",
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"adversarial": "train/0/adv_1_labelfalse_pred1_SquareAttack.png",
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"original": ["train/0/orig_1_labelfalse_SquareAttack.png"],
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"label": 0,
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"prediction": 1
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}
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```
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---
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## **🔹 Attack Types**
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The dataset contains both black-box and non-black-box adversarial attacks.
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### **1️⃣ Black-Box Attacks**
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These attacks do not require access to model gradients:
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#### **🔹 HopSkipJump Attack**
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- Query-efficient black-box attack that estimates gradients
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- Based on decision boundary approximation
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#### **🔹 Zoo Attack**
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- Zeroth-order optimization (ZOO) attack
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- Estimates gradients via finite-difference methods
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### **2️⃣ Non-Black-Box Attacks**
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These attacks require access to model gradients:
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#### **🔹 SimBA (Simple Black-box Attack)**
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- Uses random perturbations to mislead the model
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- Reduces query complexity
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#### **🔹 Boundary Attack**
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- Query-efficient attack moving along decision boundary
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- Minimizes perturbation size
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#### **🔹 Spatial Transformation Attack**
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- Uses rotation, scaling, and translation
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- No pixel-level perturbations required
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---
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## Usage
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```python
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from datasets import load_dataset
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import torch
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from torchvision import transforms
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from PIL import Image
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# Load the dataset
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dataset = load_dataset("your-org/adversarial_pcam")
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# Define transformation
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transform = transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.ToTensor()
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])
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# Process samples
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def process_sample(example):
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img = Image.open(example["image"]).convert("RGB")
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return {"image": transform(img), "label": example["label"]}
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print("Label:", label)
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print("Image shape:", img_tensor.shape) # Should be (3, 224, 224)
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```
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## **📊 Attack Success Rates**
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Success rates for each attack on the target model:
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```json
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{
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"HopSkipJump": {"success_rate": 14},
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"Zoo_Attack": {"success_rate": 22},
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"SimBA": {"success_rate": 99},
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"Boundary_Attack": {"success_rate": 98},
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"SpatialTransformation_Attack": {"success_rate": 99}
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}
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```
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## Citation
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```bibtex
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@article{lensai2025adversarial,
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title={Adversarial PCAM Dataset},
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author={LensAI Team},
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year={2025},
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url={https://huggingface.co/datasets/lens-ai/adversarial_pcam}
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}
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```
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| 1 |
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[
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| 2 |
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| 14 |
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| 16 |
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| 17 |
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| 24 |
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{
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| 25 |
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| 26 |
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| 27 |
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| 28 |
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| 29 |
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| 33 |
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| 34 |
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| 35 |
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{
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| 36 |
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| 37 |
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| 48 |
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| 102 |
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| 103 |
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| 105 |
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|
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| 109 |
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| 110 |
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| 111 |
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| 112 |
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{
|
| 113 |
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"attack": "Boundary_Attack",
|
| 114 |
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|
| 115 |
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|
| 116 |
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|
| 117 |
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| 118 |
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|
| 119 |
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|
| 120 |
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|
| 121 |
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|
| 122 |
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|
| 123 |
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{
|
| 124 |
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"attack": "Boundary_Attack",
|
| 125 |
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"type": "black_box_attacks",
|
| 126 |
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|
| 127 |
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|
| 128 |
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| 129 |
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| 130 |
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| 131 |
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| 132 |
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| 133 |
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| 134 |
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{
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| 135 |
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"attack": "Boundary_Attack",
|
| 136 |
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"type": "black_box_attacks",
|
| 137 |
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|
| 138 |
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|
| 139 |
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| 140 |
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| 141 |
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| 142 |
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| 143 |
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| 144 |
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| 145 |
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{
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| 146 |
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|
| 147 |
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|
| 148 |
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|
| 149 |
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| 150 |
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| 519 |
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{
|
| 520 |
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|
| 521 |
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|
| 522 |
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|
| 523 |
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| 524 |
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| 525 |
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|
| 526 |
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| 527 |
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| 528 |
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|
| 529 |
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| 530 |
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{
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| 531 |
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|
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|
| 533 |
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|
| 534 |
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|
| 535 |
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| 536 |
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| 537 |
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| 538 |
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| 539 |
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|
| 540 |
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| 541 |
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{
|
| 542 |
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| 544 |
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| 545 |
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| 546 |
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| 548 |
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| 550 |
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|
| 552 |
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{
|
| 553 |
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|
| 554 |
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|
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| 557 |
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| 563 |
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| 570 |
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| 571 |
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| 572 |
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| 573 |
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|
| 574 |
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{
|
| 575 |
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|
| 576 |
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|
| 577 |
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| 578 |
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| 579 |
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| 580 |
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| 581 |
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| 582 |
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| 583 |
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| 584 |
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| 585 |
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| 586 |
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|
| 588 |
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| 589 |
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| 594 |
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| 595 |
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| 596 |
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| 597 |
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| 600 |
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|
| 606 |
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| 607 |
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| 608 |
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| 609 |
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|
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| 611 |
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| 612 |
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| 616 |
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| 618 |
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{
|
| 619 |
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| 620 |
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|
| 621 |
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| 622 |
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| 623 |
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| 626 |
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| 627 |
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| 628 |
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| 629 |
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| 632 |
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perturbations/perturbation_0_Boundary_Attack.png
ADDED
|
perturbations/perturbation_0_Enhanced_Square_Attack.png
ADDED
|
perturbations/perturbation_0_HopSkipJump_Attack.png
ADDED
|
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
|
perturbations/perturbation_3_SimBA.png
ADDED
|
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
|
perturbations/perturbation_4_HopSkipJump_Attack.png
ADDED
|
perturbations/perturbation_4_SimBA.png
ADDED
|
perturbations/perturbation_4_SpatialTransformation_Attack.png
ADDED
|
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
|
perturbations/perturbation_6_Enhanced_Square_Attack.png
ADDED
|
perturbations/perturbation_6_HopSkipJump_Attack.png
ADDED
|
perturbations/perturbation_6_SimBA.png
ADDED
|
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
|
perturbations/perturbation_7_SimBA.png
ADDED
|
perturbations/perturbation_7_SpatialTransformation_Attack.png
ADDED
|
perturbations/perturbation_7_Zoo_Attack.png
ADDED
|