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原文連結
論文資訊
- 類型：已發表論文
- 日期：2021
摘要
Adversarial examples for 神經 網絡 image classifiers are known to be transferable: examples optimized to be misclassified by a source classifier are often misclassified as well by classifiers with different architectures. However, targeted adversarial examples—optimized to be classified as a chosen target class—tend to be less transferable between architectures. While prior research on constructing transferable targeted attacks has focused on improving the optimization procedure, in this work we examine the role of the source classifier. Here, we show that train- ing the source classifier to be “slightly robust”—that is, robust to small-magnitude adversarial examples—substantially improves the transferability of targeted at- tacks, even between architectures as different as convolutional 神經 網絡
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