NEO: Navigating Entropy in Optimized Closed-Box Video Adversarial Attacks

Summary

NEO is a closed-box adversarial attack on deep learning video recognition systems that uses information-entropy guidance to cut query costs. The authors model query-based attacks as an open-box approximation and find that queries near decision boundaries carry the most information. NEO combines diffusion-based perturbation initialization with mutual-information-driven optimization and reports significantly higher efficiency and stealthiness than existing methods on four benchmark datasets.

Topics

• Adversarial machine learning

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