Deepfake Detection for Facial Images with Facemasks

TL;DR

This paper evaluates the effectiveness of current deepfake detection models on masked faces and proposes two methods, with face-crop showing superior performance for detecting masked deepfakes.

Contribution

It introduces two novel approaches, face-patch and face-crop, to improve deepfake detection accuracy on masked faces, addressing a gap in existing research post-COVID-19.

Findings

Face-crop outperforms face-patch in detection accuracy.

Proposed methods enhance deepfake detection on masked faces.

Evaluation on various datasets confirms effectiveness of face-crop.

Abstract

Hyper-realistic face image generation and manipulation have givenrise to numerous unethical social issues, e.g., invasion of privacy,threat of security, and malicious political maneuvering, which re-sulted in the development of recent deepfake detection methods with the rising demands of deepfake forensics. Proposed deepfake detection methods to date have shown remarkable detection performance and robustness. However, none of the suggested deepfake detection methods assessed the performance of deepfakes with the facemask during the pandemic crisis after the outbreak of theCovid-19. In this paper, we thoroughly evaluate the performance of state-of-the-art deepfake detection models on the deepfakes with the facemask. Also, we propose two approaches to enhance the masked deepfakes detection: face-patch and face-crop. The experimental evaluations on both methods are assessed through the base-line deepfake detection models on the various deepfake datasets. Our extensive experiments show that, among the two methods, face-crop performs better than the face-patch, and could be a train method for deepfake detection models to detect fake faces with facemask in real world.