Rethinking Frequency Modeling: Tail-Aware Dynamic Adversarial Training for Long-Tailed Robustness

Summary

This paper proposes Tail-Aware Dynamic Adversarial Training (TAD-AT) to improve adversarial robustness when training data follows a long-tailed class distribution. The authors report that class frequency alone does not predict adversarial vulnerability, and that adversarial training under long-tailed data is unstable for tail classes. TAD-AT combines a frequency- and accuracy-aware training loss, a class-wise vulnerability-adjusted attack, and class-adaptive weight averaging, with experiments on long-tailed benchmarks showing significant robustness gains.

Related items

• InfoImage watermarking robustness under MLLM-guided semantic editing: Benchmark, defense, and diagnosisSimilar attack · Elsevier Security Journals
• LowYour Robot's Safety Functions Already Work. What If the Input Lies?Similar attack · VicOne Blog
• InfoSpeech Tokenizers are Vulnerable: Transferable Semantic Attack and Robust TokenizerSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• InfoAdversarial Retraining for Machine Learning-Based Man-in-the-Middle Attack Detection in Smart Grid Industrial Control SystemsSimilar attack · OpenAlex (peer-reviewed AI security)
• LowMeasuring Robustness and Efficiency in a Connectome-Constrained Fly Visual System Model: Linking an Adversarial and a Perceptual IllusionSimilar attack · Arxiv (cs.RO + cs.CV security)