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[2505.03646] GRILL: Restoring Gradient Signal in Ill-Conditioned Layers for More Effective Adversarial Attacks on Autoencoders
Summary
The paper presents GRILL, a method to enhance adversarial attacks on autoencoders by restoring gradient signals in ill-conditioned layers, improving attack effectiveness.
Why It Matters
As adversarial robustness in deep learning models, particularly autoencoders, is under-explored, this research provides critical insights into enhancing attack strategies. Understanding these vulnerabilities can lead to stronger defenses and more resilient AI systems.
Key Takeaways
- GRILL addresses the issue of vanishing gradients in ill-conditioned layers of autoencoders.
- The method significantly improves the effectiveness of norm-bounded adversarial attacks.
- Empirical evidence suggests similar vulnerabilities in modern multimodal architectures.
- This research contributes to the evaluation of adversarial robustness in deep learning models.
- Understanding these vulnerabilities is essential for developing more robust AI systems.
Computer Science > Machine Learning arXiv:2505.03646 (cs) [Submitted on 6 May 2025 (v1), last revised 23 Feb 2026 (this version, v4)] Title:GRILL: Restoring Gradient Signal in Ill-Conditioned Layers for More Effective Adversarial Attacks on Autoencoders Authors:Chethan Krishnamurthy Ramanaik, Arjun Roy, Tobias Callies, Eirini Ntoutsi View a PDF of the paper titled GRILL: Restoring Gradient Signal in Ill-Conditioned Layers for More Effective Adversarial Attacks on Autoencoders, by Chethan Krishnamurthy Ramanaik and 3 other authors View PDF HTML (experimental) Abstract:Adversarial robustness of deep autoencoders (AEs) has received less attention than that of discriminative models, although their compressed latent representations induce ill-conditioned mappings that can amplify small input perturbations and destabilize reconstructions. Existing white-box attacks for AEs, which optimize norm-bounded adversarial perturbations to maximize output damage, often stop at suboptimal attacks. We observe that this limitation stems from vanishing adversarial loss gradients during backpropagation through ill-conditioned layers, caused by near-zero singular values in their Jacobians. To address this issue, we introduce GRILL, a technique that locally restores gradient signals in ill-conditioned layers, enabling more effective norm-bounded attacks. Through extensive experiments across multiple AE architectures, considering both sample-specific and universal attacks under both standard and ...
