Generalized Synthetic Image Detection with Enhanced RGB-Noise Representation Learning

2026-07-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Image Forensics · Depth: Advanced, extended

Summary

RNSIDNet is a novel forensic framework designed for generalized synthetic image detection, addressing limitations of existing methods in cross-model generalization and real-world degradations. It employs a dual-branch architecture, integrating global RGB semantics from an attention-refined CLIP backbone with high-frequency noise artifacts captured by Bayar convolutions, dynamically modulated via a Feature-wise Linear Modulation (FiLM) module. To enhance representation learning, RNSIDNet utilizes a Hard Sample-aware Contrastive Learning (HSCL) strategy, penalizing challenging training samples to maximize the discriminative margin between pristine and synthetic domains. Extensive experiments across eight public benchmark datasets demonstrate state-of-the-art performance, achieving an average ACC of 83.81% and AUC of 92.72%, with 13.58M trainable parameters and 104.31G FLOPs.

Key takeaway

For AI Security Engineers or Machine Learning Engineers developing robust forgery detection systems, RNSIDNet offers a blueprint for superior generalization. You should consider integrating dual-modality feature fusion, specifically combining high-level semantics with low-level noise residuals, and implement hard sample-aware contrastive learning. This approach significantly improves detection against advanced generative models and real-world image degradations, ensuring your systems remain effective against evolving threats.

Key insights

RNSIDNet enhances synthetic image detection by fusing RGB semantics and noise artifacts with hard sample-aware contrastive learning.

Principles

• Dual-branch fusion of RGB semantics and noise artifacts improves detection.
• Hard sample-aware contrastive learning sharpens decision boundaries.
• Multi-source training prevents architecture-specific biases.

Method

RNSIDNet uses a CLIP backbone for RGB features, Bayar convolutions for noise, FiLM for dynamic fusion, and HSCL for optimization, trained on a multi-source dataset (AMSID) with paired alternating sampling.

In practice

• Employ Bayar convolutions for stable high-pass filtering.
• Use FiLM for dynamic, context-aware feature modulation.
• Implement HSCL to focus optimization on hard negative samples.

Topics

• Synthetic Image Detection
• AI-Generated Content
• Image Forensics
• Contrastive Learning
• Feature Fusion
• RNSIDNet
• CLIP

Code references

• multimediaFor/RNSIDNet

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.