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Undercover Deepfakes: Detecting Fake Segments in Videos by Saha, Sanjay; Perera, Rashindrie; Seneviratne, Sachith; Malepathirana, Tamasha; Rasnayaka, Sanka; Geethika, Deshani; Sim, Terence; Halgamuge, Saman is a scholarly article available to read on EtoBox.

What is Undercover Deepfakes: Detecting Fake Segments in Videos about?

The recent renaissance in generative models, driven primarily by the advent of diffusion models and iterative improvement in GAN methods, has enabled many creative applications. However, each advancement is also accompanied by a rise in the potential for misuse. In the arena of the deepfake generation, this is a key societal issue. In particular, the ability to modify segments of videos using such generative techniques creates a new paradigm of deepfakes which are mostly real videos altered slightly to distort the truth. This paradigm has been under-explored by the current deepfake detection methods in the academic literature. In this paper, we present a deepfake detection method that can address this issue by performing deepfake prediction at the frame and video levels. To facilitate testing our method, we prepared a new benchmark dataset where videos have both real and fake frame sequences with very subtle transitions. We provide a benchmark on the proposed dataset with our detection method which utilizes the Vision Transformer based on Scaling and Shifting to learn spatial features, and a Timeseries Transformer to learn temporal features of the videos to help facilitate the inte

Author

Saha, Sanjay; Perera, Rashindrie; Seneviratne, Sachith; Malepathirana, Tamasha; Rasnayaka, Sanka; Geethika, Deshani; Sim, Terence; Halgamuge, Saman

Published

2023

Language

EN