Lightweight AI Face‐Swapping Detection Method Based on Multidimensional Feature Fusion

ABSTRACT To address the critical security challenges in AI face‐swap detection—particularly the trade‐off between model lightweighting and robustness enhancement, coupled with insufficient generalization against adversarial attacks—this study proposes a security‐oriented multidimensional feature fusion framework for real‐time forgery identification. First, five complementary feature dimensions—mirror similarity, geometric asymmetry, frequency‐domain anomalies, image quality artifacts, and DeepFace confidence deviation—are integrated into a security‐oriented feature set. This set targets inherent vulnerabilities in face‐swap forgeries, enabling multiperspective identification of tampered faces and reducing the risk of missed detections or false positives in security‐sensitive scenarios; Second, Design of an adaptive security threshold mechanism based on dynamic confidence intervals, which continuously optimizes decision boundaries through online statistical modeling to strengthen defense against unseen attacks; Finally, Implementation of a lightweight random forest ensemble learning paradigm that delivers millisecond‐level inference while maintaining interpretability. Rigorous evaluations on benchmark datasets (FaceForensics++ and Celeb‐DF) demonstrate superior performance over conventional ResNet50 approaches, achieving 95.1% average accuracy and 93.4% F1‐score with merely 68 ms per‐frame processing latency. Notably, our framework exhibits exceptional security resilience in countering advanced deepfake assaults.