Search papers, labs, and topics across Lattice.
This paper introduces FakeIDet3-DB, a novel database designed to enhance the detection of digital manipulations on real government-issued IDs, addressing the challenges posed by privacy regulations in training forensic models. By employing a Pseudo-Anonymized Contextual patch Extraction (PACE) algorithm, the authors effectively extract nearly 5.2 million patches from over 6,400 images while ensuring compliance with data protection standards. The evaluation reveals that existing state-of-the-art models struggle significantly with detection and localization of both generative and classical attacks, achieving only 32.45% EER and 83.48% AUC-ROC, underscoring the need for improved forensic tools in ID authentication.
Existing forensic models fail to detect over 67% of digital manipulations on IDs, highlighting a critical vulnerability in identity verification systems.
Identity document (ID) authentication relies on the structural integrity of complex, high-frequency security patterns. However, advanced Generative AI models can now inject localized, high-fidelity manipulations, creating deceptive attacks that bypass standard verification. Training robust image forensic models to detect these anomalies is hindered by privacy regulations, forcing reliance on synthetic templates lacking the intricate visual patterns of real IDs. To bridge this domain gap, we introduce FakeIDet3-DB, the first comprehensive database of digital manipulations on real, government-issued IDs. FakeIDet3-DB encompasses classical (e.g., copy-move) and Generative AI-driven manipulations (e.g., face-swapping, inpainting) enhanced with advanced image refinement procedures to suppress visual artifacts. In addition, to comply with strict data protection regulations (e.g., GDPR), we adopt a recently-proposed framework based on patches. In order to maximize forensic utility, we formulate privacy-aware patch extraction from a real ID as a geometrically constrained image processing problem. We propose PACE, a Pseudo-Anonymized Contextual patch Extraction algorithm, which leverages Integral Image mapping and distance-driven Non-Maximum Suppression (NMS). PACE efficiently contours anonymization masks that prevent Personally Identifiable Information (PII) leakage while maximizing semantic density in peri-censorship regions, yielding almost 5.2M patches extracted from more than 6.4K images from real/fake IDs. Furthermore, an extensive evaluation of the proposed FakeIDet3-DB is performed using state-of-the-art models, showcasing they all struggle to detect and locate attacks coming from generative and classic techniques (32.45\% EER in detection and 83.48\% AUC-ROC in localization).
