Face Forgery Detection Based on Facial Micro-Movements
With the rapid development of deep learning,facial video forgery techniques have become increasingly sophisticated,posing a significant threat to social security.Although image-based facial video authenticity detection methods have achieved remarkable progress and demonstrated certain robustness and generalization capabilities,existing video stream-based methods often suffer from high input dimensionality and substantial computational overhead,which remains inadequately addressed.To tackle these challenges,this paper proposes a facial video authenticity detection method based on multivariate time series analysis.Specifically,a novel modeling approach based on facial micro-movements is designed to convert video streams into multivariate time series,effectively reducing input dimensionality.Furthermore,an enhanced Transformer network is developed to improve its ability to model time series features.Experimental results show that the proposed method achieves performance comparable to state-of-the-art approaches in terms of accuracy and generalization,demonstrating promising application potential.
face forgery detectionTransformer networkmultivariate time seriesattention mechanismdeepfakes
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