Test-time prompt adaptation hardens vision-language models against adversarial attack
Vision-language models like CLIP show strong zero-shot performance but crumble under adversarial attack. Researchers propose RITA, a test-time adaptation framework that exploits a key structural insight: while adversarial noise corrupts individual samples, the semantic relationships across augmented views remain intact. By shifting from sample-level confidence scoring to distribution-aware prompt adjustment, RITA improves robustness without retraining. This work matters because it exposes a practical gap in how production VLMs handle distribution shift, and offers a lightweight inference-time fix that could harden deployed systems against both adversarial and natural domain drift.
Modelwire context
ExplainerRITA's key insight is that adversarial corruption is sample-specific, not structural. By scoring confidence across augmented views rather than individual predictions, the method sidesteps retraining entirely, making it deployable to frozen production models.
This complements the Foveated Dynamic Transformer work from the same day, which bakes robustness into the architecture itself through biological inductive biases. RITA takes the opposite path: it assumes the model is fixed and adapts the inference procedure instead. Together, these papers sketch a spectrum of robustness strategies, from architectural (FDT) to post-hoc (RITA). The practical difference matters for practitioners: FDT requires retraining; RITA works on any CLIP-like model already in production. Neither approach requires adversarial training, signaling a broader shift away from expensive adversarial data collection.
If RITA's robustness gains hold on naturally corrupted datasets (like ImageNet-C or real-world distribution shifts) at the same magnitude as adversarial benchmarks, that confirms the distribution-aware scoring is doing real work. If performance degrades significantly on natural shift, the method may be overfitted to the adversarial threat model.
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MentionsCLIP · RITA · Vision-Language Models
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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “Robustifying Vision-Language Models via Test-Time Prompt Adaptation”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.