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Recent research has shown that Deep Neural Networks (DNNs) are highlyvulnerable to adversarial samples, which are highly transferable and can beused to attack other unknown black-box models. To improve the transferabilityof adversarial samples, several feature-based adversarial attack methods havebeen proposed to disrupt neuron activation in the middle layers. However,current state-of-the-art feature-based attack methods typically requireadditional computation costs for estimating the importance of neurons. Toaddress this challenge, we propose a Singular Value Decomposition (SVD)-basedfeature-level attack method. Our approach is inspired by the discovery thateigenvectors associated with the larger singular values decomposed from themiddle layer features exhibit superior generalization and attention properties.Specifically, we conduct the attack by retaining the decomposed Top-1 singularvalue-associated feature for computing the output logits, which are thencombined with the original logits to optimize adversarial examples. Ourextensive experimental results verify the effectiveness of our proposed method,which can be easily integrated into various baselines to significantly enhancethe transferability of adversarial samples for disturbing normally trained CNNsand advanced defense strategies. The source code of this study is available athttps://github.com/WJJLL/SVD-SSA

Boosting Adversarial Transferability via Fusing Logits of Top-1 Decomposed Feature