Authors: Gan Pei, Jiangning Zhang, Menghan Hu, Guangtao Zhai, Chengjie Wang, Zhenyu Zhang, Jian Yang, Chunhua Shen, Dacheng Tao

Published on: March 26, 2024

Impact Score: 8.0

Arxiv code: Arxiv:2403.17881

Summary

• What is new: This paper provides a comprehensive review of the latest developments in both deepfake generation and detection technologies, with a focus on summarizing the state of the art and analyzing advancements.
• Why this is important: The need for evolved detection technologies to combat the potential misuse of deepfakes in privacy invasion and phishing attacks.
• What the research proposes: A unified review of task definitions, datasets, metrics, and technology frameworks, along with a focused discussion on four mainstream deepfake fields and their detection.
• Results: Benchmarking of representative methods across popular datasets showed advancements and gaps in current detection capabilities.

Technical Details

Technological frameworks used: Comprehensive review framework for deepfake technologies, including generation and detection

Models used: Analysis of current state-of-the-art models in face swap, face reenactment, talking face generation, and facial attribute editing

Data used: Evaluation on popular datasets for deepfake generation and detection

Potential Impact

Cybersecurity firms, social media platforms, digital content creators, and companies in the identity verification sector could be significantly impacted or benefit from these insights.

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