Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection

• URL: http://arxiv.org/abs/2607.08674v1
• Date: Thu, 09 Jul 2026 16:36:54 GMT
• Title: Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection
• Abstract summary: GenRes is a novel framework for generative residual learning via a neural tensor network.<n>GenRes++ employs a learnable attention mechanism to aggregate relational features across multiple transformed samples.<n>Experiments on multiple benchmark datasets demonstrate that the proposed GenRes++ approach outperforms existing methods.
• Score: 2.2672499384619993
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid advancement of generative AI has enabled the creation of highly realistic deepfake media, posing significant threats, including misinformation, digital identity theft, fraud, and manipulation of public opinion. AI-generated image (AIGI) detection is reliably challenging due to the diversity of generative methods and the subtle artifacts they leave behind. In this work, we propose GenRes, a novel framework for generative residual learning via a neural tensor network, which models fine-grained relational features between original and transformed samples to enhance generalization. To address scenarios involving multiple generative transformations, we introduce GenRes++, which employs a learnable attention mechanism to aggregate relational features across multiple transformed samples and enables the model to focus on the most informative cues. Both models leverage PE-Core as a feature extractor, providing generalized and semantically rich embeddings that improve cross-domain performance and enable the detection of AIGI generated by unseen methods. Comprehensive experiments on multiple benchmark datasets demonstrate that the proposed GenRes++ approach outperforms existing methods.

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