A Deepfake Detection Algorithm Based on Fourier Transform of Biological Signal

作     者：Yin Ni Wu Zeng Peng Xia Guang Stanley Yang Ruochen Tan 

作者机构：School of Electrical and Electronic EngineeringWuhan Polytechnic UniversityWuhan430023China School of Mathematics and Computer ScienceWuhan Polytechnic UniversityWuhan430048China Paul G.Allen School of Computer Science and EngineeringUniversity ofWashingtonSeattleWA98195USA School of Computer Science and EngineeringUniversity of CaliforniaSanDiegoCA92093USA 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第79卷第6期

页      面：5295-5312页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Nature Science Foundation of China(Grant Number:61962010) 

主　　题：Deepfake detector remote photoplethysmography fast fourier transform spatial attention mechanism 

摘      要：Deepfake-generated fake faces,commonly utilized in identity-related activities such as political propaganda,celebrity impersonations,evidence forgery,and familiar fraud,pose new societal *** current deepfake generators strive for high realism in visual effects,they do not replicate biometric signals indicative of cardiac *** this gap,many researchers have developed detection methods focusing on biometric *** methods utilize classification networks to analyze both temporal and spectral domain features of the remote photoplethysmography(rPPG)signal,resulting in high detection ***,in the spectral analysis,existing approaches often only consider the power spectral density and neglect the amplitude spectrum—both crucial for assessing cardiac *** introduce a novel method that extracts rPPG signals from multiple regions of interest through remote photoplethysmography and processes them using Fast Fourier Transform(FFT).The resultant time-frequency domain signal samples are organized into matrices to create Matrix Visualization Heatmaps(MVHM),which are then utilized to train an image classification ***,we explored various combinations of time-frequency domain representations of rPPG signals and the impact of attention *** experimental results show that our algorithm achieves a remarkable detection accuracy of 99.22%in identifying fake videos,significantly outperforming mainstream algorithms and demonstrating the effectiveness of Fourier Transform and attention mechanisms in detecting fake faces.