A Research on Deepfake Face Detection Techniques Based on Multimodal Biometric Cross – Verification

The rapid advancement of deepfake technology poses severe threats to social security and information authenticity, as traditional single-modal detection methods face bottlenecks due to their vulnerability to circumvention. This paper systematically reviews deepfake face detection techniques based on multimodal biometric cross-verification, analyzing theoretical foundations, technical approaches, datasets, and challenges. Theoretically, it integrates visual features (facial micro-expressions, corneal specular highlights), auditory features (speech spectra, lip-sync consistency), and physiological signals (heart rate rhythms, facial blood flow), leveraging modal complementarity and consistency verification mechanisms to capture cross-modal forgery traces. Technically, it summarizes feature extraction methods such as CNN-based texture analysis, spectrogram modeling, and near-infrared imaging, and compares early fusion, late weighted voting fusion, and attention-guided dynamic fusion strategies—where attention mechanisms significantly enhance sensitivity to complex cues. It also organizes multimodal datasets (e.g., IAV-DF, DECRO) and evaluation metrics (accuracy, F1-score), providing standardized benchmarks. Although multimodal detection has improved robustness, it still faces challenges such as high-fidelity forgeries threatening modal consistency and inadequate adaptability to complex scenarios. Future research should focus on fine-grained biometric mining, lightweight model deployment, interpretability enhancement, and the improvement of regulations and technical standards to curb misuse and promote legitimate applications in digital security.