Generalized Synthetic Image Detection with Enhanced RGB-Noise Representation Learning
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 dua
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- PossiblePossibly related (embedding) · 47%NVIDIA-ISAAC-ROS/isaac_ros_object_detection →
- LinkedLinked via arxiv author · 85%Zizhen Li →
“Generalized Synthetic Image Detection with Enhanced RGB-Noise Representation Learning”
- LinkedLinked via arxiv author · 85%Gang Cao →
“Generalized Synthetic Image Detection with Enhanced RGB-Noise Representation Learning”
- LinkedLinked via arxiv author · 85%Xintian Zhang →
“Generalized Synthetic Image Detection with Enhanced RGB-Noise Representation Learning”
- LinkedLinked via arxiv author · 85%Lifang Yu →
“Generalized Synthetic Image Detection with Enhanced RGB-Noise Representation Learning”
- LinkedLinked via arxiv author · 85%Shaowei Weng →
“Generalized Synthetic Image Detection with Enhanced RGB-Noise Representation Learning”