Transferable Dual-Domain Feature Importance Attack against AI-Generated Image Detector

• URL: http://arxiv.org/abs/2511.15571v1
• Date: Wed, 19 Nov 2025 16:03:15 GMT
• Title: Transferable Dual-Domain Feature Importance Attack against AI-Generated Image Detector
• Authors: Weiheng Zhu, Gang Cao, Jing Liu, Lifang Yu, Shaowei Weng,
• Abstract summary: Recent AI-generated image (AIGI) detectors achieve impressive accuracy under clean condition.<n>It is significant to develop advanced adversarial attacks for evaluating the security of such detectors.<n>This letter proposes a Dual-domain Feature Importance Attack scheme to invalidate AIGI detectors to some extent.
• Score: 32.543253278021446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent AI-generated image (AIGI) detectors achieve impressive accuracy under clean condition. In view of antiforensics, it is significant to develop advanced adversarial attacks for evaluating the security of such detectors, which remains unexplored sufficiently. This letter proposes a Dual-domain Feature Importance Attack (DuFIA) scheme to invalidate AIGI detectors to some extent. Forensically important features are captured by the spatially interpolated gradient and frequency-aware perturbation. The adversarial transferability is enhanced by jointly modeling spatial and frequency-domain feature importances, which are fused to guide the optimization-based adversarial example generation. Extensive experiments across various AIGI detectors verify the cross-model transferability, transparency and robustness of DuFIA.

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