Physiological Signals as a Forensic Modality for Talking-Face Deepfake Detection

ForensicNet: Lightweight Attention-Enhanced MobileNetV2 for Automated Face Identification

In forensic environments, automated identification of perpetrators is difficult due to pose changes, changes in light, occlusion, and lack of labeled data. This paper presents ForensicNet, a lightweight deep learning framework for forensic face recognition that enhances attention. The suggested model combines the MobileNetV2 backbone with Convolutional Block Attention Modules (CBAM) to improve the learning of discriminative features while maintaining computational speed. A two-phase transfer learning strategy with adaptive layer unfreezing is used to improve domain adaptation and reduce overfitting. This study used publicly available datasets such as LFW and SCFace, with 15,000 facial images spanning 68 identity classes. The proposed model outperforms baseline architectures such as AlexNet, ResNet-50, and MobileNetV2, with an accuracy of 92.4%, a precision of 90.8%, and a recall of 89.5%. Additionally, the framework requires only 2.1 GFLOPs per inference, and hence can be used in real-time forensic surveillance applications.

Facial dynamics: enhancing deepfake detection via spatio-temporal motion analysis

BlinkNet: GRU-Based Temporal Analysis for Deepfake Video Detection

The increasing realism of synthetic facial videos has reduced the reliability of detectors that depend only on visible artifacts in isolated frames. This paper presents BlinkNet, an explainable deepfake-detection framework that examines the spatial appearance and temporal kinematics of eye blinks. The system detects a face, localizes 68 facial landmarks, extracts normalized ocular crops, and computes the Eye Aspect Ratio (EAR) for each frame. Overlapping sequences of 20 frames are processed by a dual-stream Temporal-Spatial Physiological Blink Anomaly Network (TPBAN). A lightweight MobileNetV2 encoder models local visual inconsistencies, while a bidirectional gated recurrent unit models the forward and backward dynamics of eyelid motion. Temporal attention assigns a relevance weight to every frame and supports frame-level anomaly visualization. Training uses a multi-task objective for authenticity classification and blink-phase recognition, together with a class-weighted binary cross-entropy term to address the imbalance between genuine and manipulated sequences. On the FaceForensics++ c23 test partition, BlinkNet obtained 83.16% accuracy, 87.31% ROC-AUC, 96.02% average precision, and a 21.08% equal error rate. The manipulated class achieved 0.90 precision and 0.88 recall. The implementation processed video at approximately 52 frames per second on a consumer laptop GPU and was integrated into a Flask-based forensic dashboard. These results indicate that ocular dynamics can complement spatial evidence while improving efficiency and interpretability.

Intelligent multimodal face anti-spoofing detection system based on biometrics

With the widespread deployment of face recognition systems, high-fidelity spoofing attacks such as photo replays, video replays, and 3D masks pose significant security threats. Current face detection approaches (e.g., CNN-based methods) often fail to adequately capture the distinct properties of different modalities during feature fusion, resulting in persistent security risks within face detection systems. This paper proposes a multi-dimensional feature fusion-based face anti-spoofing detection method based on the YOLOv8 architecture. Unlike traditional methods that rely solely on static RGB input, the paper integrates dynamic optical flow features (capturing micro-movements) with static texture analysis. By utilizing a cross-scene dataset containing diverse attack samples ,the method achieves robust detection through the fusion of spatial and temporal cues. Additionally, data augmentation strategies are employed to help the model better localize and identify targets across different scales. The proposed method exhibits strong practical value for intelligent systems in complex scenarios.

Multimodal Deepfake Detection for Digital Forensics: A Robust Audio-Visual Inconsistency Approach for Evidence Integrity

The rapid proliferation of Generative AI (GenAI) has democratized the creation of hyper-realistic multimedia forgeries, posing severe threats to electronic Know Your Customer (eKYC) systems and digital forensic investigations. While visual synthesis has reached near-perfection, maintaining precise synchronization between lip movements (visemes) and speech signals (phonemes) remains a formidable challenge. To address this, we propose a novel Multimodal Deep Learning framework designed to detect high-fidelity Deepfakes by exploiting audio visual temporal inconsistencies. Beyond traditional feature fusion, our architecture integrates a Contrastive Synchronization Loss with a Transformer based Cross-Modal Attention mechanism. This hybrid objective explicitly enforces intra-class compactness for authentic pairs while amplifying the distance for asynchronous forgeries. Extensive experiments on FaceForensics++, DFDC, and a custom Vietnamese dataset (Vn-eKYC-Aug) demonstrate that our model achieves state-of-the-art performance, maintaining high robustness against video compression and environmental noise, though operational efficacy remains sensitive to extreme low-light conditions and diverse regional dialects. This research provides a resilient forensic layer for digital identity verification, ensuring evidence integrity in the GenAI era.

PAttSBiL: an efficient deep fake video detection using integrated deep learning methodologies