README.md

Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples

Preprint available at https://arxiv.org/abs/2106.09947.

The code is developed with SecML library.

For computing the indicators, run the following command:

python3 -m src.ioaf_demo --model <model_id> --samples <num_samples>

For a complete list of models, run:

python3 -m src.ioaf_demo --help

For a complete example, run run_all.sh.

The indicators will be stored as .csv files in the results directory.

Models used in the paper

• k-WTA: https://github.com/a554b554/kWTA-Activation
• Distillation: adapted from the code found at https://github.com/carlini/nn_robust_attacks
• Ensemble Diversity: https://github.com/P2333/Adaptive-Diversity-Promoting
• TWS: adapted from https://github.com/s-huu/TurningWeaknessIntoStrength
• Input Transformations: adapted from the code found at https://github.com/eth-sri/adaptive-auto-attack
• JPEG-Compression: adapted from the code found at https://github.com/eth-sri/adaptive-auto-attack
• DNR: https://secml.readthedocs.io/en/stable/tutorials/12-DNR.html
• Standard: standard model from https://robustbench.github.io/
• Adversarial Training: Engstrom2019Robustness model from https://robustbench.github.io/

Other sources

• Adaptive attacks: https://github.com/wielandbrendel/adaptive_attacks_paper
• Adaptive AutoAttack: https://github.com/eth-sri/adaptive-auto-attack
• RobustBench: https://robustbench.github.io/