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

TL;DR

ForensicFormer introduces a hierarchical multi-scale transformer framework that significantly improves cross-domain image forgery detection accuracy, robustness, and interpretability over existing methods, addressing the challenges posed by diverse manipulation techniques.

Contribution

The paper proposes a novel hierarchical multi-scale transformer architecture that unifies artifact detection, boundary analysis, and semantic reasoning for robust cross-domain forgery detection.

Findings

Achieves 86.8% accuracy across diverse datasets, outperforming prior methods.

Demonstrates robustness to JPEG compression with 83% accuracy at Q=70.

Provides pixel-level forgery localization with a 0.76 F1-score.

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.