LORE: Lagrangian-Optimized Robust Embeddings for Visual Encoders

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.

@article{khodabandeh2025_2505.18884,
  title={ LORE: Lagrangian-Optimized Robust Embeddings for Visual Encoders },
  author={ Borna Khodabandeh and Amirabbas Afzali and Amirhossein Afsharrad and Seyed Shahabeddin Mousavi and Sanjay Lall and Sajjad Amini and Seyed-Mohsen Moosavi-Dezfooli },
  journal={arXiv preprint arXiv:2505.18884},
  year={ 2025 }
}