Related papers: A Classifier-Agnostic Zero-Shot Adversarial Attack Detection via CLIP

A Classifier-Agnostic Zero-Shot Adversarial Attack Detection via CLIP

URL: http://arxiv.org/abs/2606.30342v2
Date: Tue, 30 Jun 2026 08:10:27 GMT
Title: A Classifier-Agnostic Zero-Shot Adversarial Attack Detection via CLIP
Abstract summary: Adversarial attacks pose a challenge to the reliability of deep learning models.<n>We propose a completely black-box, zero-shot adversarial attack detection framework that utilizes prompt-based similarity scores derived from CLIP.<n>Experiments validate that $A4D$ achieves SOTA detection results in the attack-agnostic and classifier-agnostic setting.
Score: 7.308896004974025
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Adversarial attacks pose a challenge to the reliability of deep learning models, motivating effective detection methods. Existing techniques often rely on attack-specific assumptions, access to adversarial samples, or knowledge of the underlying classifier (white-box). We propose $A^4D$ Attack- and Architecture-Agnostic Adversarial Detector, a completely black-box, zero-shot adversarial attack detection framework that utilizes prompt-based similarity scores derived from CLIP. To the best of our knowledge this is the first attempt to utilize CLIP for such a task. The method is based on two key observations: (i) CLIP is sensitive even to small imperceptible non-semantic perturbations; (ii) The shift in CLIP embedding space is not arbitrary and can be used as a robust attack indicator. Experiments across multiple attacks, datasets and classifiers validate that $A^4D$ achieves SOTA detection results in the attack-agnostic and classifier-agnostic setting.

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