ForensicFormer: Hierarchical Multi-Scale Reasoning for Cross-Domain Image Forgery Detection

• URL: http://arxiv.org/abs/2601.08873v1
• Date: Mon, 12 Jan 2026 04:29:36 GMT
• Title: ForensicFormer: Hierarchical Multi-Scale Reasoning for Cross-Domain Image Forgery Detection
• Abstract summary: ForensicFormer is a hierarchical framework that unifies low-level artifact detection, mid-level boundary analysis, and high-level semantic reasoning.<n>Our method maintains 86.8% average accuracy across seven diverse test sets, spanning traditional manipulations, GAN-generated images, and diffusion model outputs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The proliferation of AI-generated imagery and sophisticated editing tools has rendered traditional forensic methods ineffective for cross-domain forgery detection. We present ForensicFormer, a hierarchical multi-scale framework that unifies low-level artifact detection, mid-level boundary analysis, and high-level semantic reasoning via cross-attention transformers. Unlike prior single-paradigm approaches, which achieve <75% accuracy on out-of-distribution datasets, our method maintains 86.8% average accuracy across seven diverse test sets, spanning traditional manipulations, GAN-generated images, and diffusion model outputs - a significant improvement over state-of-the-art universal detectors. We demonstrate superior robustness to JPEG compression (83% accuracy at Q=70 vs. 66% for baselines) and provide pixel-level forgery localization with a 0.76 F1-score. Extensive ablation studies validate that each hierarchical component contributes 4-10% accuracy improvement, and qualitative analysis reveals interpretable forensic features aligned with human expert reasoning. Our work bridges classical image forensics and modern deep learning, offering a practical solution for real-world deployment where manipulation techniques are unknown a priori.

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