Deepfake Forensics: Identifying Real Regions in Altered Videos with Digital Watermarking

Authors

  • Aayush Asthana Dougherty Valley High School San Ramon
  • Sam Saarinen Brown University

DOI:

https://doi.org/10.47611/jsr.v12i4.2209

Keywords:

Digital Watermarking, Deepfake detection, Video cryptography, Face detection, faceswap, RSA Encryption, Asymmetric Encryption, Big-O Notation

Abstract

This paper describes a method for detecting Deepfake videos using a lightweight yet secure video encryption algorithm. With the increasing use of digital media, transferring data via the Internet or other mediums requires protection. In the proposed method a digital signature is generated and encrypted using Asymmetric Encryption (RSA). This encrypted signature is then used as a blind watermark for the video. This technique aims to detect “face swap” type of Deepfake videos. It is an efficient algorithm and has minimal impact on the perceptibility of the video quality.

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References or Bibliography

“Doctored Pelosi video highlights the threat of deepfake tech”

https://www.cbsnews.com/video/doctored-pelosi-video-highlights-the-threat-of-deepfake-tech/

“A deepfake video of Mark Zuckerberg presents a new challenge for Facebook” https://www.cnn.com/2019/06/11/tech/zuckerberg-deepfake/index.html

“The State of Deepfakes in 2020”

https://www.skynettoday.com/overviews/state-of-deepfakes-2020

Dolhansky, Brian, et al. "The deepfake detection challenge (dfdc) dataset." arXiv preprint arXiv:2006.07397 (2020).

Masood, Momina, et al. "Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward." Applied Intelligence (2022): 1-53.

Haliassos, Alexandros, et al. "Lips don't lie: A generalisable and robust approach to face forgery detection." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021.

Prabhishek Singh, R S Chadha ,“A Survey of Digital Watermarking Techniques, Applications and Attacks”, International Journal of Engineering and Innovative Technology (IJEIT), Volume 2, Issue 9, March 2013

DFDC Dataset: https://www.kaggle.com/competitions/deepfake-detection- challenge/data

MTCNN PyTorch: https://www.kaggle.com/code/timesler/guide-to-mtcnn- in-facenet-pytorch

Published

11-30-2023

How to Cite

Asthana, A., & Saarinen, . S. (2023). Deepfake Forensics: Identifying Real Regions in Altered Videos with Digital Watermarking. Journal of Student Research, 12(4). https://doi.org/10.47611/jsr.v12i4.2209

Issue

Section

Research Projects