arXiv:2606.00738v1 Announce Type: new Abstract: Adversarial Training (AT) is a leading defense against adversarial examples but often suffers from Catastrophic Overfitting (CO) in efficient single-step variants, where robustness to multi-step attacks collapses despite high single-step performance. We address this failure mode with two contributions. First, we formalize Epsilon Overfitting (EO), a perspective in which fixed perturbation magnitudes and directions exacerbate CO, and show that introducing perturbation variability significantly improves robust generalization across different archit
The continuous evolution of AI models necessitates robust defenses against adversarial attacks, making advancements in adversarial training crucial and timely.
Improving the resilience of AI systems against 'catastrophic overfitting' directly enhances their reliability and security, particularly for mission-critical applications.
The ability to develop more robust AI models with efficient adversarial training techniques could lead to more trustworthy and deployable AI in real-world scenarios.
- · AI developers
- · Cybersecurity sector
- · Organizations deploying AI
- · Adversarial attackers
- · AI systems lacking robustness
More secure AI deployments become possible due to enhanced adversarial robustness.
Increased trust in AI systems could accelerate adoption across sensitive industries like defense and finance.
The arms race between AI defense and attack mechanisms intensifies, driving further innovation in both areas.
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Read at arXiv cs.LG