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.

翻译：随着复杂生成式AI模型的激增，身份文档中的合成篡改威胁显著升级，尤其是通过人脸替换和文本修复攻击。本文提出TwoHead-SwinFPN，这是一种统一的深度学习架构，可同时对身份文档中的篡改区域进行二元分类和精确定位。该方法将Swin Transformer主干网络与特征金字塔网络（FPN）及UNet风格解码器相结合，并通过卷积块注意力模块（CBAM）增强特征表示能力。该模型采用双头架构，利用不确定性加权的多任务学习对检测与分割任务进行联合优化。在FantasyIDiap数据集上的大量实验表明，该方法在分类任务上取得84.31%准确率和90.78% AUC，在定位任务上获得57.24%平均Dice分数的优异性能。所提方法在二元分类任务中达到88.61%的F1分数，同时通过FastAPI实现保持了适用于实际部署的计算效率。我们的综合评估包括消融实验、跨设备泛化分析，以及对10种语言和3种采集设备的详细性能评估。