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Adversarial Robustness in PyTorch Article Series

This project is a collection of articles with accompanying PyTorch code introducing and discussing adversarial examples, adversarial training and confidence-calibrated adversarial training:

• Monitoring PyTorch Training with Tensorboard
• Easily Saving and Loading PyTorch Models
• 2.56% on Cifar10 with AutoAugment
• $L_p$ Adversarial Examples on Cifar10
• Adversarial Training on Cifar10
• Confidence-Calibrated Adversarial Training on Cifar10
• Proper Robustness Evaluation
• Distal Adversarial Examples
• Adversarial Patches and Frames
• Adversarial transformations

Large parts of this repository are taken from my ICML'20 [1] and ICCV'21 [2] papers as well as my student's ECCV'21 workshop paper [3]:

• [1] D. Stutz, M. Hein, B. Schiele. Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks. ICML, 2020.
• [2] D. Stutz, M. Hein, B. Schiele. Relating Adversarially Robust Generalization to Flat Minima. ICCV, 2021.
• [3] S. Rao, D. Stutz, B. Schiele. Adversarial Training Against Location-Optimized Adversarial Patches ECCV Workshops, 2020.