BlinkNet: GRU-Based Temporal Analysis for Deepfake Video Detection

Abstract

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.