Generalized Synthetic Image Detection with Enhanced RGB-Noise Representation Learning

• URL: http://arxiv.org/abs/2607.06354v1
• Date: Tue, 07 Jul 2026 14:52:22 GMT
• Title: Generalized Synthetic Image Detection with Enhanced RGB-Noise Representation Learning
• Abstract summary: We propose RNSIDNet, a novel forensic framework that achieves robust detection through enhanced RGB-Noise representation learning.<n>Specifically, our method employs a dual-branch architecture where global RGB semantics, extracted by an attention-refined CLIP backbone, dynamically modulate high-frequency noise artifacts captured by Bayar convolutions via a Feature-wise Linear Modulation (FiLM) module.<n>To further enhance the learned representations, we design a Hard Sample-aware Contrastive Learning (HSCL) strategy.
• Score: 33.97020349697987
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advancement of large-scale generative models has accelerated the spread of highly deceptive AI-generated images, making generalized synthetic image detection a critical imperative. Existing forensic networks often struggle with cross-model generalization and realworld degradations due to their reliance on single-domain representations and conventional binary classification optimization. To overcome these limitations, we propose RNSIDNet, a novel forensic framework that achieves robust detection through enhanced RGB-Noise representation learning. Specifically, our method employs a dual-branch architecture where global RGB semantics, extracted by an attention-refined CLIP backbone, dynamically modulate highfrequency noise artifacts captured by Bayar convolutions via a Feature-wise Linear Modulation (FiLM) module. To further enhance the learned representations, we design a Hard Sample-aware Contrastive Learning (HSCL) strategy. By explicitly penalizing challenging training samples, HSCL reshapes the latent feature space to maximize the discriminative margin between pristine and synthetic domains. Extensive experiments across eight public benchmark datasets verify that our model achieves state-of-the-art performance, delivering superior generalization ability, robustness, and computational efficiency. Code and dataset will be publicly available on https://github.com/multimediaFor/RNSIDNet.

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