Detection of compressed DeepFake video drawbacks and technical developments

• Main Authors: Humidan, Amna-Saga, Abdullah, Lili Nurliyana, Abdul Halin, Alfian
• Format: Conference or Workshop Item
• Published: IEEE 2022
• Online Access: http://psasir.upm.edu.my/id/eprint/44240/; https://ieeexplore.ieee.org/document/10002433

The rapid advancement in artificial intelligence (AI) has revolutionized the creation of synthesized multimedia and given rise to DeepFake, a highly realistic fake video or image depicting a person doing or saying something he/ she has never done or said in reality. Attackers use DeepFake to tarnish individuals’ reputations and disseminate fake news, which in turn undermines societies’ stability and security. In response to this cyber security threat, many DeepFake detection methods have been proposed, which show outstanding performance in detecting high-quality DeepFake videos. However, their performance decreases when detecting compressed fake videos. This article investigates the problem of compressed DeepFake video detection. Firstly, popular detection methodologies are reviewed focusing on their abilities to distinguish between real and fake compressed video footage. Then, we attempt to identify and discuss the weaknesses of the methods by examining factors that contribute to decreasing detection efficiency. At the end of this article, we present new generation DeepFake detector techniques that reportedly exhibit improved performance and robustness against video compression. We hope that the contribution from this work inspires innovation for more reliable solutions to combat potential security threats posed by DeepFake videos.