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The rapid evolution of deepfake technology in recent years brings forth both promising opportunities and formidable challenges. This research paper conducts a thorough exploration of cutting-edge techniques in deepfake generation and detection, shedding light on the dynamic landscape of this field. Our investigation into deepfake generation includes an in-depth examination of advanced approaches such as CramerGAN, StyleGAN, Progressive Growing GAN (PGGAN), IcGAN, SaGAN, and HifaFace, among others. Each method, from the precision in facial attribute control offered by StyleGAN to the exceptional image quality attained by PGGAN, presents unique capabilities. The paper systematically assesses the effectiveness of ProGAN, Convolutional long short-term memory (LSTM) based residual network (CLRNet), AutoGAN, Octave convolution operation (OctConv), and F3-Net in identifying deepfake content. Beyond these techniques, we also delve into various emerging generation and detection strategies, providing a comprehensive overview of the diverse approaches in the ever-evolving landscape of deepfake technology.
Gupta et al. (Fri,) studied this question.