用分层注意力模型检测跨域图像伪造，准确率超86%。
ForensicFormer: Hierarchical Multi-Scale Reasoning for Cross-Domain Image Forgery Detection
- 分三层分析：像素级痕迹、边界异常、语义逻辑，用交叉注意力融合
- 跨7类数据集平均准确率达86.8%，压缩后仍保持83%精度
- 可定位伪造区域，结果符合人工专家判断，适合未知伪造场景
AI生成图像和高级编辑工具的普及使传统取证方法在跨域检测中失效。我们提出ForensicFormer，一种分层多尺度框架，通过交叉注意力变换器统一低层痕迹检测、中层边界分析和高层语义推理。相比以往单一范式方法在分布外数据集上低于75%的准确率，本方法在七个多样化测试集（涵盖传统篡改、GAN生成图像及扩散模型输出）上保持86.8%平均准确率，显著优于现有通用检测器。在JPEG压缩（Q=70）下仍达83%准确率（基线仅66%），并实现像素级伪造定位，F1-score为0.76。大量消融实验表明，每一层组件贡献4-10%准确率提升；定性分析显示其提取的特征与人工专家推理一致。本工作融合经典图像取证与现代深度学习，为未知伪造技术的真实场景部署提供可行方案。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。