DeSI: Deepfake Source Identifier for Social Media

Kartik Narayan, Harsh Agarwal, Surbhi Mittal, Kartik Thakral, Suman Kundu, Mayank Vatsa, Richa Singh; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 2858-2867

Social media holds the power to influence a significant change in the population. Through social media, people all around the world can connect and share their views. However, this social space is now infected due to the infiltration of fraudulent, obscene, fake and possibly, influential media. According to a UNESCO report, prevalence of fake news and deepfake content possess the potential of spreading fake propaganda and can lead to political and social unrest. Trust on social media is an emerging problem and there is an urgent need to address the same. There has been some research around approaches that detect fake news and deepfakes, however, identification of the source of these deepfakes posted on social media platforms is an equally important but relatively unexplored challenge. This paper proposes a novel Deepfake Source Identification (DeSI) algorithm that identifies the sources of deepfakes posted on Twitter. The proposed DeSI algorithm allows for two input modalities - text and images. We rigorously test our algorithm in both constrained and unconstrained experimental setups and report the observed results. In the constrained setting, the algorithm correctly identifies all the deepfake tweets as well their sources. The complete framework is further encased in a web portal to facilitate intuitive use and analysis of the results.