Deepfake detection in videos with multiple faces using geometric-fakeness features

TL;DR

This paper introduces a novel deepfake detection method using geometric-fakeness features and temporal analysis, effectively identifying deepfakes in videos with multiple faces, outperforming existing methods on benchmark datasets.

Contribution

The paper proposes a new approach utilizing geometric-fakeness features and deep learning to improve deepfake detection in multi-face videos, addressing limitations of current methods.

Findings

Outperforms state-of-the-art on FaceForensics++, DFDC, Celeb-DF, WildDeepFake

Effective in videos with multiple faces and objects

Maintains accuracy across different deepfake techniques

Abstract

Due to the development of facial manipulation techniques in recent years deepfake detection in video stream became an important problem for face biometrics, brand monitoring or online video conferencing solutions. In case of a biometric authentication, if you replace a real datastream with a deepfake, you can bypass a liveness detection system. Using a deepfake in a video conference, you can penetrate into a private meeting. Deepfakes of victims or public figures can also be used by fraudsters for blackmailing, extorsion and financial fraud. Therefore, the task of detecting deepfakes is relevant to ensuring privacy and security. In existing approaches to a deepfake detection their performance deteriorates when multiple faces are present in a video simultaneously or when there are other objects erroneously classified as faces. In our research we propose to use geometric-fakeness features (GFF) that characterize a dynamic degree of a face presence in a video and its per-frame deepfake scores. To analyze temporal inconsistencies in GFFs between the frames we train a complex deep learning model that outputs a final deepfake prediction. We employ our approach to analyze videos with multiple faces that are simultaneously present in a video. Such videos often occur in practice e.g., in an online video conference. In this case, real faces appearing in a frame together with a deepfake face will significantly affect a deepfake detection and our approach allows to counter this problem. Through extensive experiments we demonstrate that our approach outperforms current state-of-the-art methods on popular benchmark datasets such as FaceForensics++, DFDC, Celeb-DF and WildDeepFake. The proposed approach remains accurate when trained to detect multiple different deepfake generation techniques.