Curls & Whey: Boosting Black-Box Adversarial Attacks

By Yucheng Shi, Siyu Wang, Yahong Han. 2019

126 citations recorded by the source corpus.

Image classifiers based on deep neural networks suffer from harassment caused by adversarial examples. Two defects exist in black-box iterative attacks that generate adversarial examples by incrementally adjusting the noise-adding direction for each step. On the one hand, existing iterative attacks add noises monotonically along the direction of gradient ascent, resulting in a lack of diversity and adaptability of the generated iterative trajectories. On the other hand, it is trivial to perform adversarial attack by adding excessive noises, but currently there is no refinement mechanism to squeeze redundant noises. In this work, we propose Curls \& Whey black-box attack to fix the above two defects. During Curls iteration, by combining gradient ascent and descent, we `curl' up iterative trajectories to integrate more diversity and transferability into adversarial examples. Curls iteration also alleviates the diminishing marginal effect in existing iterative attacks. The Whey optimization further squeezes the `whey' of noises by exploiting the robustness of adversarial perturbation. Extensive experiments on Imagenet and Tiny-Imagenet demonstrate that our approach achieves impressive decrease on noise magnitude in l2 norm. Curls & Whey attack also shows promising transferability against ensemble models as well as adversarially trained models. In addition, we extend our attack to the targeted misclassification, effectively reducing the difficulty of targeted attacks under black-box condition.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Safety, Robustness & Interpretability

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Citations in the corpus

Adversarial Example Games (2020, methodology): “In addition to non-interactive blackbox adversaries, we compare against there exists multiple hybrid approach’s that combine crafting attacks on surrogate models which then serve as a good initialization point for queries to the target model [54, 58, 37].”
Advances in adversarial attacks and defenses in computer vision: A survey (2021, background): “Other recent examples focusing directly or indirectly on improving transferablity of perturbations include [116], [117], [118], [119], [120].”