统一模型同时检测与定位身份证伪造，准确率超84%
TwoHead-SwinFPN: A Unified DL Architecture for Synthetic Manipulation, Detection and Localization in Identity Documents
- 双头结构联合优化分类与定位任务，融合注意力机制提升特征表达
- 在FantasyIDiap数据集上分类准确率84.31%，定位Dice达57.24%
- 支持多语言跨设备部署，适合真实场景的快速检测应用
生成式AI模型的普及显著加剧了身份文档中的合成篡改威胁，尤其是人脸替换和文本修复攻击。本文提出TwoHead-SwinFPN，一种统一的深度学习架构，可同时完成身份证件的二分类检测与篡改区域精确定位。该方法结合Swin Transformer主干、特征金字塔网络（FPN）与UNet风格解码器，并引入卷积块注意力模块（CBAM）增强特征表示。采用双头结构实现检测与分割任务的联合优化，通过不确定性加权多任务学习策略。在FantasyIDiap数据集上的实验表明，分类准确率达84.31%，AUC为90.78%，定位平均Dice分数为57.24%。二分类F1-score达88.61%，并通过FastAPI实现高效推理，适合实际部署。评估涵盖消融实验、跨设备泛化分析及10种语言、3类采集设备的性能测试。
原文摘要 · Abstract (English)
The proliferation of sophisticated generative AI models has significantly escalated the threat of synthetic manipulations in identity documents, particularly through face swapping and text inpainting attacks. This paper presents TwoHead-SwinFPN, a unified deep learning architecture that simultaneously performs binary classification and precise localization of manipulated regions in ID documents. Our approach integrates a Swin Transformer backbone with Feature Pyramid Network (FPN) and UNet-style decoder, enhanced with Convolutional Block Attention Module (CBAM) for improved feature representation. The model employs a dual-head architecture for joint optimization of detection and segmentation tasks, utilizing uncertainty-weighted multi-task learning. Extensive experiments on the FantasyIDiap dataset demonstrate superior performance with 84.31\% accuracy, 90.78\% AUC for classification, and 57.24\% mean Dice score for localization. The proposed method achieves an F1-score of 88.61\% for binary classification while maintaining computational efficiency suitable for real-world deployment through FastAPI implementation. Our comprehensive evaluation includes ablation studies, cross-device generalization analysis, and detailed performance assessment across 10 languages and 3 acquisition devices.
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