EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents
Abstract
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.
Type
Publication
International Conference on Computer Vision Workshop
Authors
Senior Research Scientist
Prof Sébastien Marcel (IEEE Fellow and IAPR Fellow) is a senior research scientist at
the Idiap Research Institute (Switzerland), he heads the Biometrics Security
and Privacy group and conducts research on face recognition, speaker
recognition, vein recognition, attack detection (presentation attacks,
morphing attacks, deepfakes) and template protection. He is also Professor at
the University de Lausanne (UNIL) at the School of Criminal Justice. He is
also the Director of the Swiss Center for Biometrics at Idiap, which conducts
certifications of biometric products. He was Associate Editor and Guest
Editor of IEEE journals (TBIOM, SPL, TIFS and SPM). He is also the lead
Editor of the Springer Handbook of Biometrics Anti-Spoofing (Editions 1, 2
and 3). Since June 2025 he is a member of the Idiap Direction ad interim.