GreedyPixel: Fine-Grained Black-Box Adversarial Attack Via Greedy Algorithm
Hanrui Wang, Ching-Chun Chang, Chun-Shien Lu, Christopher Leckie, and Isao Echizen

TL;DR
GreedyPixel is a novel black-box adversarial attack method that uses a greedy, pixel-wise optimization approach to generate highly precise, sparse, and imperceptible perturbations, achieving state-of-the-art success rates.
Contribution
It introduces a brute-force, per-pixel greedy optimization technique guided by a surrogate model, improving the precision and effectiveness of black-box attacks.
Findings
Achieved state-of-the-art success rates on CIFAR-10 and ImageNet.
Produced visually imperceptible, pixel-wise sparse perturbations.
Bridged the gap between black-box practicality and white-box performance.
Abstract
Deep neural networks are highly vulnerable to adversarial examples, which are inputs with small, carefully crafted perturbations that cause misclassification -- making adversarial attacks a critical tool for evaluating robustness. Existing black-box methods typically entail a trade-off between precision and flexibility: pixel-sparse attacks (e.g., single- or few-pixel attacks) provide fine-grained control but lack adaptability, whereas patch- or frequency-based attacks improve efficiency or transferability, but at the cost of producing larger and less precise perturbations. We present GreedyPixel, a fine-grained black-box attack method that performs brute-force-style, per-pixel greedy optimization guided by a surrogate-derived priority map and refined by means of query feedback. It evaluates each coordinate directly without any gradient information, guaranteeing monotonic loss reduction…
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