مرکز منطقه ای اطلاع رسانی علوم و فناوری Journal of Information Systems and Telecommunication (JIST) 2322-1437 9 35 2021 7 23 DeepFake Detection using 3D-Xception Net with Discrete Fourier Transformation DeepFake Detection using 3D-Xception Net with Discrete Fourier Transformation 161 168 10.52547/jist.9.35.161 en Adeep Biswas VIT, Vellore Debayan Bhattacharya VIT, Vellore Kakelli Anil Kumar Vellore Institute of Technology, Vellore 2020 8 18 The videos are more popular for sharing content on social media to capture the audience’s attention. The artificial manipulation of videos is growing rapidly to make the videos flashy and interesting but they can easily misuse to spread false information on social media platforms. Deep Fake is a problematic method for the manipulation of videos in which artificial components are added to the video using emerging deep learning techniques. Due to the increase in the accuracy of deep fake generation methods, artificially created videos are no longer detectable and pose a major threat to social media users. To address this growing problem, we have proposed a new method for detecting deep fake videos using 3D Inflated Xception Net with Discrete Fourier Transformation. Xception Net was originally designed for application on 2D images only. The proposed method is the first attempt to use a 3D Xception Net for categorizing video-based data. The advantage of the proposed method is, it works on the whole video rather than the subset of frames while categorizing. Our proposed model was tested on the popular dataset Celeb-DF and achieved better accuracy. http://jist.ir/fa/Article/Download/15558