arXiv:2605.26929v1 Announce Type: new Abstract: Adversarial training (AT) remains one of the most reliable empirical defenses against adversarial attacks. Its robustness critically depends on how the underlying min-max objective is optimized. In practice, Stochastic Gradient Descent (SGD) optimizer remains the default optimization choice for AT, whereas adaptive optimizers often improve standard training but may yield inferior robustness. Recently, the Muon optimizer, which orthogonalizes matrix-valued updates via an approximate polar decomposition, has achieved notable success in large-scale
The continuous evolution of adversarial attacks necessitates ongoing research into more robust defense mechanisms against AI vulnerabilities.
Improved optimizer techniques can significantly enhance the robustness of AI models, which is crucial for their deployment in sensitive applications and for maintaining trust in AI systems.
The potential adoption of Muon optimizer over SGD for adversarial training could lead to more resilient AI models against adversarial attacks, altering best practices for AI security.
- · AI researchers
- · AI security solution providers
- · Organizations deploying AI
- · Adversarial attackers
More robust AI models against malicious inputs.
Increased confidence in AI deployments within high-stakes environments.
Potential for new adversarial attack vectors to emerge as defenses improve, leading to an arms race in AI security.
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Read at arXiv cs.LG