A Note on Implementation Errors in Recent Adaptive Attacks Against Multi-Resolution Self-Ensembles

• URL: http://arxiv.org/abs/2501.14496v1
• Date: Fri, 24 Jan 2025 13:52:37 GMT
• Title: A Note on Implementation Errors in Recent Adaptive Attacks Against Multi-Resolution Self-Ensembles
• Authors: Stanislav Fort,
• Abstract summary: This note documents an implementation issue in recent adaptive attacks against the multi-resolution self-ensemble defense. When attacks are properly constrained within the intended bounds, the defense maintains non-trivial robustness. Our analysis reveals an intriguing finding: properly bounded adaptive attacks against strong multi-resolution self-ensembles often align with human perception.
• Score: 7.681029509871509
• License:
• Abstract: This note documents an implementation issue in recent adaptive attacks (Zhang et al. [2024]) against the multi-resolution self-ensemble defense (Fort and Lakshminarayanan [2024]). The implementation allowed adversarial perturbations to exceed the standard $L_\infty = 8/255$ bound by up to a factor of 20$\times$, reaching magnitudes of up to $L_\infty = 160/255$. When attacks are properly constrained within the intended bounds, the defense maintains non-trivial robustness. Beyond highlighting the importance of careful validation in adversarial machine learning research, our analysis reveals an intriguing finding: properly bounded adaptive attacks against strong multi-resolution self-ensembles often align with human perception, suggesting the need to reconsider how we measure adversarial robustness.

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