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Contract And Conquer: How to Provably Compute Adversarial Examples for a Black-Box Model?
🤖AI Summary
Researchers propose Contract And Conquer (CAC), a new method for provably generating adversarial examples against black-box neural networks using knowledge distillation and search space contraction. The approach provides theoretical guarantees for finding adversarial examples within a fixed number of iterations and outperforms existing methods on ImageNet datasets including vision transformers.
Key Takeaways
- →CAC introduces the first provable method for generating adversarial examples in black-box settings with transferability guarantees.
- →The method combines knowledge distillation on expanding datasets with precise contraction of adversarial search spaces.
- →Experimental results show superior performance compared to state-of-the-art black-box attack methods on ImageNet.
- →The approach successfully targets modern architectures including vision transformers.
- →The research addresses a key limitation in existing adversarial attack methods that lack theoretical guarantees.
#adversarial-attacks#black-box#neural-networks#knowledge-distillation#computer-vision#machine-learning#security#robustness#vision-transformers
Read Original →via arXiv – CS AI
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