Proposed DeepFake Detection Method Using Multiwavelet Transform


  • Saadi Mohammed Saadi Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad, Iraq
  • Waleed Ameen Mahmoud Al-Jawher Department of Electronics and Communication Engineering Uruk University Baghdad, Iraq



DeepFake Video, Generative Adversarial Networks (GAN), MultiWavelet, Artificial Neural Network


Videos made by artificial intelligence (A.I.) seem real, but they are not. When making DeepFake videos, face-swapping methods are frequently employed. The misuse of technology when using fakes, even though it was fun at first, these videos were somewhat recognizable to human eyes. However, as machine learning advanced, it became simpler to produce profound fake videos. It's practically impossible to tell it apart from actual videos now. Using GANs (Generative Adversarial Networks) and other deep learning techniques, DeepFake videos are output technology that may mislead people into thinking something is real when it is not. This study used a MultiWavelet transform to analyze the type of edge and its sharpness to develop a blur inconsistency detecting system. With this capability, it can assess whether or not the facial area is obscured in the video. As a result, it will detect fake videos. This paper reviews DeepFake detection techniques and discusses how they might be combined or altered to get more accurate results. A detection rate of more than 93.5% was obtained, which is quite successful.




How to Cite

Mohammed Saadi, S., & Mahmoud Al-Jawher, W. A. (2023). Proposed DeepFake Detection Method Using Multiwavelet Transform. International Journal of Innovative Computing, 13(1-2), 61–66.