A survey on deep learning based reenactment methods for deepfake applications - Ramamurthy Dhanyalakshmi; Claudiu‐Ionut Popirlan; Duraisamy Jude Hemanth (IET Image Processing)
Abstract Among the sectors that deep learning has transformed, deepfake, a novel method of manipulating multimedia, deserves particular attention. The long‐term objective of many researchers is to seamlessly mimic human facial movement or whole‐body activity, referred to as reenactment. Deepfake progress has made this goal much more feasible in recent years. Yet, achieving more realistic facial and body reenactment remains a challenging task. The primary focus of this study is to explore the current capability of the reenactment techniques and expand them further to attain greater results. The analysis offers a thorough overview of the various techniques involved, the challenges addressed, the datasets utilized, and the metrics employed by the underlying methods of reenactment technologies. The study also addresses the potential risks and their mitigating strategies to ensure responsible reenactment techniques. To the best of the authors' knowledge, this is the first survey paper that delves deeper into the topic of deepfake reenactment.