Abstract
Adversarial attacks and defenses disregard the lack of invariance of convolutional neural networks (CNNs), that is, the inability of CNNs to classify samples and their symmetric transformations the same. The lack of invariance of CNNs with respect to symmetry transformations is detrimental when classifying transformed original samples but not necessarily detrimental when classifying transformed adversarial samples. For original images, the lack of invariance means that symmetrically transformed original samples are classified differently from their correct labels. However, for adversarial images, the lack of invariance means that symmetrically transformed adversarial images are classified differently from their incorrect adversarial labels. Might the CNN lack of invariance revert symmetrically transformed adversarial samples to the correct classification? This paper answers this question affirmatively for a threat model that ranges from zero-knowledge adversaries to perfect-knowledge adversaries. We base our defense against perfect-knowledge adversaries on devising a Klein four symmetry subgroup that incorporates an additional artificial symmetry of pixel intensity inversion. The closure property of the subgroup not only provides a framework for the accuracy evaluation but also confines the transformations that an adaptive, perfect-knowledge adversary can apply. We find that by using only symmetry defense, no adversarial samples, and by changing nothing in the model architecture and parameters, we can defend against white-box PGD adversarial attacks, surpassing the PGD adversarial training defense by up to ~50% even against a perfect-knowledge adversary for ImageNet. The proposed defense also maintains and surpasses the classification accuracy for non-adversarial samples.
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URL
https://arxiv.org/abs/2210.04087
