Field-Localized Forgery Detection for Digital Identity Documents

Abstract

Digital onboarding and eKYC systems used by banks, fintech platforms, telecom providers, and other third-party services commonly verify users by comparing an uploaded identity document with a selfie or live facial capture. This workflow is convenient, but it also makes verification systems vulnerable to localised document manipulations, such as replacing the facial photograph, editing textual identity fields, or altering both. Existing image-forgery detectors are largely designed for natural images and do not explicitly account for the structured layout of identity documents, where security-relevant information is concentrated in specific semantic fields. We propose FLiD, a lightweight field-localised framework for identity-document forgery detection. Instead of processing the full document image, FLiD localises the facial and textual regions using a fine-tuned YOLO11 detector, extracts compact representations with a frozen MobileNetV3-Small backbone, and classifies field-level forgeries using a small 191K-parameter head. Face and text detectors are trained independently and combined through score-level fusion for documents containing simultaneous manipulations. Under 5-fold cross-validation, FLiD achieves AUC scores of 0.834, 0.926, and 0.837 for face, text, and combined attacks, respectively, while reducing Equal Error Rate by 28--29 percentage points compared with a full-document baseline. FLiD also outperforms general-purpose forgery detectors while requiring 13x fewer trainable parameters and 21x fewer FLOPs per field (~8x per full multi-field document), making it a promising candidate for data and resource constrained KYC deployment.