Understanding Audiovisual Deepfake Detection: Techniques, Challenges, Human Factors and Perceptual Insights

TL;DR

This survey reviews audiovisual deepfake detection techniques, challenges, and human factors, emphasizing the importance of multimodal analysis for improved accuracy and future research directions in cybersecurity.

Contribution

It provides a comprehensive overview of audiovisual deepfake generation, detection methods, datasets, and critically analyzes their strengths and limitations, bridging unimodal and multimodal approaches.

Findings

Multimodal analysis enhances deepfake detection accuracy.

Existing datasets support research but need expansion.

Current methods have limitations in real-world scenarios.

Abstract

Deep Learning has been successfully applied in diverse fields, and its impact on deepfake detection is no exception. Deepfakes are fake yet realistic synthetic content that can be used deceitfully for political impersonation, phishing, slandering, or spreading misinformation. Despite extensive research on unimodal deepfake detection, identifying complex deepfakes through joint analysis of audio and visual streams remains relatively unexplored. To fill this gap, this survey first provides an overview of audiovisual deepfake generation techniques, applications, and their consequences, and then provides a comprehensive review of state-of-the-art methods that combine audio and visual modalities to enhance detection accuracy, summarizing and critically analyzing their strengths and limitations. Furthermore, we discuss existing open source datasets for a deeper understanding, which can contribute to the research community and provide necessary information to beginners who want to analyze deep learning-based audiovisual methods for video forensics. By bridging the gap between unimodal and multimodal approaches, this paper aims to improve the effectiveness of deepfake detection strategies and guide future research in cybersecurity and media integrity.