Exploring Sparsity and Smoothness of Arbitrary Lp Norms in Adversarial Attacks

arXiv:2602.06578 · cs.LG, cs.AI · Submitted 2026-02-06 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-02-06

Updated: 2026-09-17

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Sparsity — 63× in this paper · explained in 4 episodes
• Adversarial Attacks — 47× in this paper · explained in 1 episode
• Transformer — 16× in this paper · explained in 2 episodes
• Adversarial Training — 14× in this paper · explained in 1 episode
• Adversarial Examples — 10× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Robustness — 4× in this paper · explained in 5 episodes
• Adversarial Robustness — 2× in this paper · explained in 3 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Projected Gradient Descent — 2× in this paper
• Adam — 1× in this paper · explained in 2 episodes
• Auto-Projected Gradient Descent — 1× in this paper
• Clustering — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes

Sources

• GreedyPixel: Fine-Grained Black-Box Adversarial Attack Via Greedy Algorithm
• Should Adversarial Attacks Use Pixel p-Norm?
• DASH: A Meta-Attack Framework for Synthesizing Effective and Stealthy Adversarial Examples
• A Versatile Framework for Designing Group-Sparse Adversarial Attacks

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