arXiv Computer Vision September 18, 2026 By Jiaming Liang, Chi-Man Pun

Fast Preemptive Robustification: High-Frequency Response Anti-Aligns Shared Vulnerability

Read the original on arXiv Computer Vision →

The paper introduces Fast Preemptive Robustification (FPR), a lightweight defense that enhances the robustness of deep neural networks against transferable adversarial attacks. By sharpening Laplacian responses through a single 3×3 channel‑wise convolution, FPR eliminates the need for surrogate models, iterative optimization, or specialized training. Experiments show that FPR lowers untargeted attack success rates by 12.7% and reduces targeted attack success from 10.7% to 4.1%.

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