Undercover Deepfakes: Detecting Fake Segments in Videos

Summary

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.

Plain-language summary

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.

Research problem

However, each advancement is also accompanied by a rise in the potential for misuse. In particular, the paradigm we address will form a powerful tool for the moderation of deepfakes, where human oversight can be better targeted to the parts of videos suspected of being deepfakes. There exist many methods to tackle the deepfake detection problem formulated as a binary classification problem [1], [5], [13].

Methodology

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 interpretation of possible deepfakes. Extensive experiments on a variety of deepfake generation methods show excellent results by the proposed method on temporal segmentation and classical video-level predictions as well. We provide a benchmark on the proposed dataset with our detection method which utilizes the Vision Transformer based on Scaling and Shifting [38] to learn spatial features, and a Timeseries Transformer to learn temporal features of the videos to help facilitate the interpretation of possible deepfakes.

Main findings

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. Undercover Deepfakes: Detecting Fake Segments in Videos Sanjay Saha∗, Rashindrie Perera†§, Sachith Seneviratne†§, Tamasha Malepathirana†, Sanka Rasnayaka∗, Deshani Geethika†, Terence Sim∗, Saman Halgamuge† ∗National University of Singapore, Singapore †University of Melbourne, Australia sanjaysaha@u.nus.edu∗, cdperera@student.unimelb.edu.au†, sachith.seneviratne@unimelb.edu.au†, tmalepathira@student.unimelb.edu.au†, sanka@nus.edu.sg∗ , dpoddenige@student.unimelb.edu.au†, terence.sim@nus.edu.sg∗, saman@unimelb.edu.au† Abstract—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. INTRODUCTION Deep learning has made significant advances over the last few years, with varying degrees of societal impact. Figure 1(a) demonstrates deepfake videos where the entire video is not fake, but some of the real frames were replaced by fake frames. This differs from classical deepfake detection where the detector makes a video level prediction as demonstrated in Figure 1(b).

Key contributions

Limitations

• However, there is a more pressing drawback in these studies that we §Equal contribution (a) Simplified illustration of sample videos from the newly introduced benchmark dataset for temporal deepfake segment detection.Classical Detector Real / Fake (video) Temporal Segment Detector Real / Fake (video) Fake segment #1 start, end Fake segment #2 start, end Fake segment #n start, end Video with real and fake segments Video with real and fake segments (b) Comparison of our method with the classical deepfake detection.

Future work

• This provides a new direction for future research. • We curated a new dataset specifically for deepfake tem- poral segmentation, which will be publicly available for researchers to evaluate their methods.
• Our rigorous experiments establish benchmark results for temporal segmentation of deepfakes, providing a baseline for future work.
• This allows maximal flexibility in analyzing content for the presence of deepfakes and additionally provides comparison points for future research along these related but separate evaluation protocols.

Models and methods

Datasets

Evaluation metrics

Code repositories

Keywords