DASH: A Meta-Attack Framework for Synthesizing Effective and Stealthy Adversarial Examples
Abdullah Al Nomaan Nafi, Habibur Rahaman, Zafaryab Haider, Tanzim Mahfuz, Fnu Suya, Swarup Bhunia, Prabuddha Chakraborty

TL;DR
DAASH is a novel meta-attack framework that combines existing Lp-based adversarial attacks to generate highly effective and perceptually aligned adversarial examples, outperforming current methods across multiple datasets.
Contribution
It introduces a fully differentiable multi-stage meta-attack framework that adaptively combines base attacks guided by a joint loss function for improved perceptual quality.
Findings
DAASH achieves higher attack success rates than state-of-the-art perceptual attacks.
It significantly improves visual quality metrics like SSIM, LPIPS, and FID.
DAASH generalizes well to unseen defenses, serving as a strong robustness evaluation baseline.
Abstract
Numerous techniques have been proposed for generating adversarial examples in white-box settings under strict Lp-norm constraints. However, such norm-bounded examples often fail to align well with human perception, and only recently have a few methods begun specifically exploring perceptually aligned adversarial examples. Moreover, it remains unclear whether insights from Lp-constrained attacks can be effectively leveraged to improve perceptual efficacy. In this paper, we introduce DAASH, a fully differentiable meta-attack framework that generates effective and perceptually aligned adversarial examples by strategically composing existing Lp-based attack methods. DAASH operates in a multi-stage fashion: at each stage, it aggregates candidate adversarial examples from multiple base attacks using learned, adaptive weights and propagates the result to the next stage. A novel meta-loss…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Advanced Malware Detection Techniques · Electrostatic Discharge in Electronics
