A Quick Look at B-cos Nets' Adversarial Robustness

Repository containing checkpoint weights and jupyter notebook for generating results in A Quick Look at B-cos Nets' Adversarial Robustness.

Work based on B-cos networks (see paper: Böhle, M., Singh, N., Fritz, M., & Schiele, B. (2023). B-cos Alignment for Inherently Interpretable CNNs and Vision Transformers. arXiv preprint arXiv:2306.10898.).

Adversarial robustness against PGD attacks

Evaluation of the attacks done using the Robustness package, from which the pretrained (Adv-)ResNet-50 models were used.

Table 1: CIFAR-10 test accuracy against PGD $\ell_\infty$ attacks:

Table 1	Standard Accuracy	$\epsilon=8/255$	$\epsilon=16/255$
ResNet-50	95.25%	0.0% / 0.0%	0.0% / 0.0%
Adv-ResNet-50	87.03%	53.49% / 53.29%	18.13% / 17.62%
Bcos-ResNet-56	88.06%	0.03% / 0.03%	0.0% / 0.0%
Bcos-ResNet-50	87.42%	19.79% / 19.10%	8.33% / 7.68%

Table 2: CIFAR-10 test accuracy against PGD $\ell_2$ attacks:

Table 2	Standard Accuracy	$\epsilon=0.25$	$\epsilon=0.5$	$\epsilon=1.0$	$\epsilon=2.0$
ResNet-50	95.25%	8.66% / 7.34%	0.28% / 0.14%	0.0% / 0.0%	0.0% / 0.0%
Adv-ResNet-50	90.83%	82.34% / 82.31%	70.17% / 70.11%	40.47% / 40.22%	5.23% / 4.97%
Bcos-ResNet-56	88.06%	35.06% / 34.75%	13.64% / 13.20%	9.02% / 8.91%	0.0% / 0.0%
Bcos-ResNet-50	87.42%	65.64% / 65.71%	50.19% / 49.96%	33.16% / 32.04%	15.01% / 14.57%