Deepfake Detection through Motion Magnification Inspired Feature Manipulation
by Aydamir Mirzayev, M.S. in Computer Engineering
Advisor: Asst. Prof. Dr. Hamdi Dibeklioğlu
The seminar will be on Wednesday, September 7, 2022 at 11:30 am
***This is an online seminar. To obtain the event link, please send a message to department.
Synthetically generated media content poses a significant threat to information security in the online domain. Manipulated videos and images of celebrities, politicians, and ordinary citizens, if aimed at misrepresentation, and defamation can cause significant damage to one’s reputation. Early detection of such content is crucial to timely alleviation of further spread of questionable information. In the past years, a significant number of deepfake detection frameworks have proposed to utilize motion magnification as a preprocessing step aimed at revealing transitional inconsistencies relevant to the prediction outcome. However, such an approach is sub-optimal since often utilized motion manipulation approaches are optimized for a limited set of controlled motions and display significant visual artifacts when used outside of their domain. To this end, rather than apply motion magnification as a separate processing step, we propose to test trainable motion magnification-inspired feature manipulation units as an addition to a convolutional-LSTM classification network. In our approach, we aim to take the first step at understanding the use of magnification-like architectures in the task of video classification rather than aim at full integration. We test out results on the Celeb-DF dataset which is composed of more than five thousand synthetically generated videos generated using DeepFakes fake generation method. To test the contribution of the proposed feature manipulation unit, we treat it as another network layer and test the performance of the network both with and without it. To ensure the consistency of our results we perform multiple experiments with the same configurations and report the average accuracy.