LORE: Lagrangian-Optimized Robust Embeddings for Visual Encoders

• URL: http://arxiv.org/abs/2505.18884v1
• Date: Sat, 24 May 2025 21:54:52 GMT
• Title: LORE: Lagrangian-Optimized Robust Embeddings for Visual Encoders
• Authors: Borna Khodabandeh, Amirabbas Afzali, Amirhossein Afsharrad, Seyed Shahabeddin Mousavi, Sanjay Lall, Sajjad Amini, Seyed-Mohsen Moosavi-Dezfooli,
• Abstract summary: We propose Lagrangian-d Robust Embeddings (LORE), a novel unsupervised adversarial fine-tuning framework.<n>LORE significantly improves zero-shot adversarial robustness with minimal degradation in clean data accuracy.
• Score: 11.01163097340578
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual encoders have become fundamental components in modern computer vision pipelines. However, ensuring robustness against adversarial perturbations remains a critical challenge. Recent efforts have explored both supervised and unsupervised adversarial fine-tuning strategies. We identify two key limitations in these approaches: (i) they often suffer from instability, especially during the early stages of fine-tuning, resulting in suboptimal convergence and degraded performance on clean data, and (ii) they exhibit a suboptimal trade-off between robustness and clean data accuracy, hindering the simultaneous optimization of both objectives. To overcome these challenges, we propose Lagrangian-Optimized Robust Embeddings (LORE), a novel unsupervised adversarial fine-tuning framework. LORE utilizes constrained optimization, which offers a principled approach to balancing competing goals, such as improving robustness while preserving nominal performance. By enforcing embedding-space proximity constraints, LORE effectively maintains clean data performance throughout adversarial fine-tuning. Extensive experiments show that LORE significantly improves zero-shot adversarial robustness with minimal degradation in clean data accuracy. Furthermore, we demonstrate the effectiveness of the adversarially fine-tuned CLIP image encoder in out-of-distribution generalization and enhancing the interpretability of image embeddings.

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