Low-Rank Adversarial PGD Attack
This work addresses adversarial robustness in computer vision, offering a more memory-efficient attack method for researchers and practitioners, though it is incremental as it builds on existing PGD techniques.
The authors tackled the problem of adversarial attacks on deep neural networks by proposing a low-rank variation of the Projected Gradient Descent (PGD) method, which performs comparably to or sometimes outperforms traditional PGD while using significantly less memory.
Adversarial attacks on deep neural network models have seen rapid development and are extensively used to study the stability of these networks. Among various adversarial strategies, Projected Gradient Descent (PGD) is a widely adopted method in computer vision due to its effectiveness and quick implementation, making it suitable for adversarial training. In this work, we observe that in many cases, the perturbations computed using PGD predominantly affect only a portion of the singular value spectrum of the original image, suggesting that these perturbations are approximately low-rank. Motivated by this observation, we propose a variation of PGD that efficiently computes a low-rank attack. We extensively validate our method on a range of standard models as well as robust models that have undergone adversarial training. Our analysis indicates that the proposed low-rank PGD can be effectively used in adversarial training due to its straightforward and fast implementation coupled with competitive performance. Notably, we find that low-rank PGD often performs comparably to, and sometimes even outperforms, the traditional full-rank PGD attack, while using significantly less memory.