Passive image forensics using universal techniques: a review

Digital tamper detection is a substantial research area of image analysis that identifies the manipulation in the image. This domain has matured with time and incredible accuracy in the last five years using machine learning and deep learning-based approaches. Now, it is time for the evolution of fusion and reinforcement-based learning techniques. Nevertheless, before commencing any experimentation, a researcher needs a comprehensive state of the art in that domain. Various directions, their outcome, and analysis form the basis for successful experiments and ensure better results. Universal image forensics approaches are a significant subset of image forensic techniques and must be explored thoroughly before experimentation. This motivated authors to write a review of these approaches. In contrast to the existing recent surveys that aim at image splicing or copy-move detection, our study aims to explore the universal type-independent techniques required to highlight image tampering. Several universal approaches based on resampling, compression, and inconsistency-based detection are compared and evaluated in the presented work. This review communicates the approach used for review, analysed literature, and lastly, the conclusive remarks. Various resources beneficial for the research community, i.e. journals and datasets, are explored and enumerated. Lastly, a futuristic reinforcement learning-based model is proposed.