Deepfake Generation and Detection: A Benchmark and Survey
Gan Pei, Jiangning Zhang, Menghan Hu, Zhenyu Zhang, Chengjie Wang, Yunsheng Wu, Guangtao Zhai, Jian Yang, Dacheng Tao
Abstract
Deepfake technology aims to synthesize highly realistic facial images and videos, with broad application potential in entertainment, film production, and digital human modeling. Deep learning has driven major progress in generative modeling, from VAEs and GANs to the recent rise of diffusion models. The latter have sparked a renewed wave of research through their superior generation quality. In addition to deepfake generation, corresponding detection technologies continuously evolve to regulate the potential misuse of deepfakes, such as privacy invasion and phishing attacks. This survey comprehensively reviews the latest developments in deepfake generation and detection, summarizing and analyzing current state-of-the-arts in this rapidly evolving field. First, we unify task definitions, comprehensively introduce datasets and metrics, and summarize the underlying technologies. Then, we review the development of several related sub-fields and examine four representative deepfake research fields: face swapping, face reenactment, talking-face generation, and facial attribute editing, as well as forgery detection. Subsequently, we benchmark representative methods on widely adopted datasets to provide a comprehensive and up-to-date evaluation of the most influential published works. Finally, we discuss the key challenges and outline future research directions for the field. We closely follow the latest developments in this project.