Detection of Digital Manipulation in Facial Images (Student Abstract)


  • Aman Mehra IIIT Delhi
  • Akshay Agarwal IIIT Delhi Texas A&M University
  • Mayank Vatsa IIT Jodhpur
  • Richa Singh IIT Jodhpur


Deepfake Detection, Bias, Media Forensics, 3D ConvNets, Motion Magnification, Computer Vision, Face Recognition


Advances in deep learning have enabled the creation of photo-realistic DeepFakes by switching the identity or expression of individuals. Such technology in the wrong hands can seed chaos through blackmail, extortion, and forging false statements of influential individuals. This work proposes a novel approach to detect forged videos by magnifying their temporal inconsistencies. A study is also conducted to understand role of ethnicity bias due to skewed datasets on deepfake detection. A new dataset comprising forged videos of Indian ethnicity individuals is presented to facilitate this study.




How to Cite

Mehra, A., Agarwal, A., Vatsa, M., & Singh, R. (2021). Detection of Digital Manipulation in Facial Images (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15845-15846. Retrieved from



AAAI Student Abstract and Poster Program