ML Security PapersGenerating and Detecting Various Types of Fake Image and Audio Content: A Review of Modern Deep Learning Technologies and Tools
Arash Dehghani , Hossein Saberi
Published on arXiv
2501.06227
Output Integrity Attack
OWASP ML Top 10 — ML09
Key Finding
Reviews the state-of-the-art in deepfake generation and detection, identifying an ongoing arms race and the urgent need for robust detection strategies against increasingly realistic synthetic media.
This paper reviews the state-of-the-art in deepfake generation and detection, focusing on modern deep learning technologies and tools based on the latest scientific advancements. The rise of deepfakes, leveraging techniques like Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion models and other generative models, presents significant threats to privacy, security, and democracy. This fake media can deceive individuals, discredit real people and organizations, facilitate blackmail, and even threaten the integrity of legal, political, and social systems. Therefore, finding appropriate solutions to counter the potential threats posed by this technology is essential. We explore various deepfake methods, including face swapping, voice conversion, reenactment and lip synchronization, highlighting their applications in both benign and malicious contexts. The review critically examines the ongoing "arms race" between deepfake generation and detection, analyzing the challenges in identifying manipulated contents. By examining current methods and highlighting future research directions, this paper contributes to a crucial understanding of this rapidly evolving field and the urgent need for robust detection strategies to counter the misuse of this powerful technology. While focusing primarily on audio, image, and video domains, this study allows the reader to easily grasp the latest advancements in deepfake generation and detection.
Key Contributions
- Comprehensive review of deepfake generation techniques (VAEs, GANs, diffusion models) across face swapping, voice conversion, reenactment, and lip sync
- Critical analysis of the arms race between deepfake generation and detection, highlighting current challenges in identifying manipulated content
- Survey of detection strategies across audio, image, and video domains with identification of future research directions
🛡️ Threat Analysis
Deepfake detection is a canonical ML09 (Output Integrity) concern — the paper reviews AI-generated content detection methods and the arms race between synthetic media generation and forensic detection across image, audio, and video modalities.