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

arXiv:2603.25244 · cs.CV · Submitted 2026-03-26 · Read on arXiv

cs.CV

Submitted: 2026-03-26

Updated: 2026-09-17

Code: https://github.com/anlthms/nips-2017

Terminology

• Robustness — 36× in this paper · explained in 5 episodes
• Accuracy — 19× in this paper · explained in 1 episode
• Transferability — 19× in this paper · explained in 4 episodes
• Adversarial Attacks — 13× in this paper · explained in 1 episode
• Adversarial Examples — 12× in this paper · explained in 2 episodes
• Adversarial Training — 11× in this paper · explained in 1 episode
• Adversarial Transferability — 11× in this paper · explained in 1 episode
• Cosine Similarity — 8× in this paper · explained in 3 episodes
• Adversarial Robustness — 6× in this paper · explained in 3 episodes
• Fast Preemptive Robustification — 5× in this paper
• Generalization — 3× in this paper · explained in 13 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Adversarial Defense — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Attack Success Rate (ASR) — 2× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Conformal Prediction — 1× in this paper · explained in 5 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Consistency Distillation — 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
• Distillation — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• MeanSparse: Post-Training Robustness Enhancement Through Mean-Centered Feature Sparsification
• Local Convolutions Cause an Implicit Bias towards High Frequency Adversarial Examples
• Rethinking Model Ensemble in Transfer-based Adversarial Attacks
• Reducing Texture Bias of Deep Neural Networks via Edge Enhancing Diffusion
• OTI: A Model-free and Visually Interpretable Measure of Image Attackability
• Robust Principles: Architectural Design Principles for Adversarially Robust CNNs
• Intriguing properties of neural networks

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