Versatile Backdoor Attack With Visible, Semantic, Sample-Specific and Compatible Triggers

Summary

Researchers introduce the Visible, Semantic, Sample-specific, and Compatible (VSSC) trigger for backdoor attacks on deep neural networks. They present an automated pipeline that selects triggers with large language models, inserts them into images with generative models, and checks insertion quality with vision-language models. The authors report that VSSC stays effective under visual distortions and works in physical scenarios using corresponding real-world objects.

Topics

• Adversarial machine learning

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