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| title: Adversarial Attack Demo | |
| emoji: "\U0001F6E1\uFE0F" | |
| colorFrom: red | |
| colorTo: yellow | |
| sdk: gradio | |
| sdk_version: "5.29.0" | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # Adversarial Attack Demo | FGSM & PGD | |
| Upload an image and watch how small, imperceptible perturbations can fool a neural network classifier. | |
| **Courses**: 215 AI Safety ch1-ch2 | |
| ## Features | |
| - FGSM (Fast Gradient Sign Method) attack | |
| - PGD (Projected Gradient Descent) iterative attack | |
| - Side-by-side comparison: original vs perturbation vs adversarial | |
| - Adjustable epsilon, step size, and iteration count | |
| - L-inf / L2 / SSIM metrics | |