This work introduces FakeIDet3-DB, the first comprehensive database of digital manipulations on real, government-issued IDs, and proposes PACE, a Pseudo-Anonymized Contextual patch Extraction algorithm, which leverages Integral Image mapping and distance-driven Non-Maximum Suppression (NMS).
Abstract
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).
In the contemporary digital landscape, the exponential proliferation of high-dimensional multimedia data across social platforms, communication networks, and biometric authentication channels is accompanied by an escalating threat of sophisticated generative deception. Deepfakes and synthetic media manipulations present critical systemic risks, ranging from targeted identity fraud to widespread misinformation campaigns. Traditional forensic methodologies—such as pixel-level error level analysis, lighting inconsistency checks, and static rule-based verification—fail to scale efficiently against modern deep synthesis techniques due to heavy compression assumptions, manual feature-engineering constraints, and computational latency. To address these challenges, this monograph presents the design and deployment of the Deepfake Forensic Suite, an automated Identity Mapping and Media Integrity Verification System. The proposed framework establishes a multi-layered security pipeline. First, it implements a high-precision biometric mapping and alignment phase utilizing Multi-task Cascaded Convolutional Networks (MTCNN) to isolate facial regions and eliminate environmental noise. Second, it leverages an optimized MobileNetV2 architecture to extract deep spatial features and compress complex visual attributes into a compact latent representation. By learning the structural characteristics of authentic human faces, the system computes principled prediction probability scores that naturally diverge when processing synthetic manipulations. Furthermore, a statistically robust tri-state classification strategy (Real, Fake, or Uncertain) is established based on validation-set confidence percentiles, enhancing forensic reliability by flagging borderline cases for manual administrative review. The performance of the system is evaluated against established baselines, including traditional Viola-Jones frameworks and shallow convolutional structures. Finally, the practical deployment-readiness of the system is demonstrated through model serialization, a real-time webcam inference API, and a reproducible, interactive web dashboard engineered entirely within the Streamlit framework. The resulting suite provides a lightweight, high-assurance digital forensics solution capable of edge-device execution without requiring slow, cloud-dependent infrastructure.
T. Manimala, P. Sravani, V. Rajitha et al.· EPRA international journal o...· 0 citations
A generative detector that localizes tampering by estimating the local restoration cost required to align a query image with authentic visual-text statistics, rather than by learning forgery-specific decision boundaries is proposed, and Sparse-Constraint Rectified Flow is introduced, a detector-oriented adaptation of Flow Matching for spatially sparse anomaly localization.
Jiangling Zhang, Shuxuan Gao, Zeyu Chen et al.· 0 citations
Fingerprint Presentation Attack Detection (PAD) is a critical component of fingerprint identification systems, serving as a protective measure against unauthorized access. In this paper, we observe that different regions of a fingerprint image can exhibit varying Artifact Extraction Difficulty (AED), with high-AED regions requiring more sophisticated extraction mechanisms to capture more subtle discriminative evidence. To address this issue, we propose to quantify AED using local Gabor feature certainty and partition fingerprint images into multiple regions based on their respective AED values. We then propose an AED guided Cascade Forgery Mining Network (CFM-Net) that employs an adaptive-depth feature extraction architecture to detect more precise and comprehensive artifact evidence across regions with heterogeneous AED values. Furthermore, we introduce an Orientation Guided Adversarial Training (OGAT) module to filter out identity information from PAD features while preserving the integrity of original artifact evidence. Experimental evaluations on LivDet datasets demonstrate the superior performance of our approach compared to state-of-the-art methods and achieve significant improvement in the classification ability of high AED fingerprints.
Hongyan Fei, Chuanwei Huang, Zheng Wang et al.· arXiv.org· 0 citations
Digital image forgery has become a critical concern in the era of advanced multimedia technologies, where the authenticity of visual content directly affects trust in digital communication, journalism, and law enforcement. Among various forgery techniques, copy-move forgery (CMF) is among the most common and deceptive, as it involves duplicating a region of an image to conceal or misrepresent information. To address this challenge, numerous copy-move forgery detection (CMFD) approaches have been proposed, ranging from block-based and keypoint-based methods to hybrid models and deep learning (DL) techniques. This paper provides a comprehensive review of these approaches, analyzing their strengths and limitations, and evaluating their performance across multiple benchmark datasets. The evaluation considers factors such as image resolution, manipulation types, and robustness against post-processing attacks. By systematically comparing the algorithms and datasets, the study highlights persistent challenges and outlines future research directions. The findings aim to guide researchers in selecting appropriate techniques and inspire the development of more robust CMFD solutions.
