# Robust Vision-Language Models via Tensor Decomposition: A Defense Against Adversarial Attacks Source: [Robust Vision-Language Models via Tensor Decomposition: A Defense Against Adversarial Attacks]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:40:29.919Z Authors: Het Patel, Muzammil Allie, Qian Zhang, Jia Chen, Evangelos E. Papalexakis Published: 2025-09-19T17:16:32.000Z ## Abstract Vision language models (VLMs) excel in multimodal understanding but are prone to adversarial attacks. Existing defenses often demand costly retraining or significant architecture changes. We introduce a lightweight defense using tensor decomposition suitable for any pre-trained VLM, requiring no retraining. By decomposing and reconstructing vision encoder representations, it filters adversarial noise while preserving meaning. Experiments with CLIP on COCO and Flickr30K show improved robustness. On Flickr30K, it restores 12.3\\% performance lost to attacks, raising Recall@1 accuracy from 7.5\\% to 19.8\\%. On COCO, it recovers 8.1\\% performance, improving accuracy from 3.8\\% to 11.9\\%. Analysis shows Tensor Train decomposition with low rank (8-32) and low residual strength ($\\alpha=0.1-0.2$) is optimal. This method is a practical, plug-and-play solution with minimal overhead for existing VLMs. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()