Forensic infrastructures need to exchange data between others and cannot work separately without sharing information. Image forensic techniques play an important role in providing solutions to these challenges. This presents a comprehensive survey of deep learning-based watermarking methods provided that no changes to the information are permitted during the transfer process. The study aims to present the operation of each, analyze the performance of the techniques and provide comparative evaluations including advantages and limitations. The results show the performance of the systems, in terms of robustness, image quality, and capacity. The research highlights that algorithms improve the performance and achieve the security requirements of content authenticity for legal proceedings and public comment that maintaining public trust.
