Generative Artificial Intelligence and the Evolving Challenge of Deepfake Detection: A Systematic Analysis

• Published in: Journal of sensor and actuator networks Vol. 14; no. 1; p. 17
• Main Authors: Babaei, Reza, Cheng, Samuel, Duan, Rui, Zhao, Shangqing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2025
• Subjects: Algorithms; Artificial intelligence; Automation; Deception; Deep learning; Deepfake; deepfake detection; Detectors; Diffusion models; digital forensics; False information; Generative adversarial networks; Generative artificial intelligence; Innovations; Literature reviews; Machine learning; media security; Neural networks; Real time; Realism; Social media; Technological change; United States; New Jersey; Germany
• ISSN: 2224-2708; 2224-2708
• DOI: 10.3390/jsan14010017

Deepfake technology, which employs advanced generative artificial intelligence to create hyper-realistic synthetic media, poses significant challenges across various sectors, including security, entertainment, and education. This literature review explores the evolution of deepfake generation methods, ranging from traditional techniques to state-of-the-art models such as generative adversarial networks and diffusion models. We navigate through the effectiveness and limitations of various detection approaches, including machine learning, forensic analysis, and hybrid techniques, while highlighting the critical importance of interpretability and real-time performance in detection systems. Furthermore, we discuss the ethical implications and regulatory considerations surrounding deepfake technology, emphasizing the need for comprehensive frameworks to mitigate risks associated with misinformation and manipulation. Through a systematic review of the existing literature, our aim is to identify research gaps and future directions for the development of robust, adaptable detection systems that can keep pace with rapid advancements in deepfake generation.