融合卷积与注意力机制，精准识别证件伪造痕迹。
EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents
- 结合轻量级卷积-变压器架构与图像噪声特征。
- 在FantasyID数据集上超越基线方法，定位准确率更高。
- 适合金融、安防等需要高精度证件验证的场景。
图像和文档编辑工具的普及使得数字证件伪造变得愈发容易，严重威胁到KYC流程和远程开户系统的安全性。本文提出EdgeDoc，一种用于检测和定位证件伪造的新方法。该模型融合了轻量级卷积-变压器结构与从图像中提取的辅助噪声特征，提升了对细微篡改的识别能力。EdgeDoc在ICCV 2025 DeepID挑战赛中获得第三名，表现出强劲竞争力。在FantasyID数据集上的实验结果表明，该方法优于现有基线模型，证实其在真实场景中的有效性。
原文摘要 · Abstract (English)
The widespread availability of tools for manipulating images and documents has made it increasingly easy to forge digital documents, posing a serious threat to Know Your Customer (KYC) processes and remote onboarding systems. Detecting such forgeries is essential to preserving the integrity and security of these services. In this work, we present EdgeDoc, a novel approach for the detection and localization of document forgeries. Our architecture combines a lightweight convolutional transformer with auxiliary noiseprint features extracted from the images, enhancing its ability to detect subtle manipulations. EdgeDoc achieved third place in the ICCV 2025 DeepID Challenge, demonstrating its competitiveness. Experimental results on the FantasyID dataset show that our method outperforms baseline approaches, highlighting its effectiveness in realworld scenarios. Project page : https://www.idiap. ch/paper/edgedoc/
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