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2018

Understanding and Enhancing the Transferability of Adversarial Examples

Wu, Lei, Zhu, Zhanxing, Tai, Cheng et al.

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State-of-the-art deep neural networks are known to be vulnerable to adversarial examples, formed by applying small but malicious perturbations to the original inputs.

Moreover, the perturbations can \textit{transfer across models}: adversarial examples generated for a specific model will often mislead other unseen models.
Consequently the adversary can leverage it to attack deployed systems without any query, which severely hinder the application of deep learning, especially in the areas where security is crucial.
In this work, we systematically study how two classes of factors that might influence the transferability of adversarial examples.

Understanding and Enhancing the Transferability of Adversarial Examples · Around

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