Diversified Adversarial Attacks based on Conjugate Gradient Method
This work addresses the problem of improving adversarial attack efficiency for security researchers, though it is incremental as it builds on existing gradient-based methods.
The paper tackled the vulnerability of deep learning models to adversarial examples by proposing the Auto Conjugate Gradient (ACG) attack, which outperformed the state-of-the-art Auto-PGD by finding more adversarial examples with fewer iterations in large-scale evaluations on robust models.
Deep learning models are vulnerable to adversarial examples, and adversarial attacks used to generate such examples have attracted considerable research interest. Although existing methods based on the steepest descent have achieved high attack success rates, ill-conditioned problems occasionally reduce their performance. To address this limitation, we utilize the conjugate gradient (CG) method, which is effective for this type of problem, and propose a novel attack algorithm inspired by the CG method, named the Auto Conjugate Gradient (ACG) attack. The results of large-scale evaluation experiments conducted on the latest robust models show that, for most models, ACG was able to find more adversarial examples with fewer iterations than the existing SOTA algorithm Auto-PGD (APGD). We investigated the difference in search performance between ACG and APGD in terms of diversification and intensification, and define a measure called Diversity Index (DI) to quantify the degree of diversity. From the analysis of the diversity using this index, we show that the more diverse search of the proposed method remarkably improves its attack success rate.