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.

数字篡改检测是图像分析的一个重要研究领域，可识别图像中的操作。在过去五年中，这个领域随着时间的推移和令人难以置信的准确性，使用机器学习和基于深度学习的方法已经成熟。现在，是时候发展融合和基于强化的学习技术了。然而，在开始任何实验之前，研究人员需要全面了解该领域的最新技术。各种方向、结果和分析构成了成功实验并确保获得更好结果的基础。通用图像取证方法是图像取证技术的一个重要子集，必须在实验前彻底探索。这促使作者写一篇关于这些方法的评论。与最近针对图像拼接或复制移动检测的现有调查相比，我们的研究旨在探索突出图像篡改所需的通用类型无关技术。在目前的工作中比较和评估了几种基于重采样、压缩和基于不一致检测的通用方法。该评论传达了用于评论的方法，分析了文献，最后是结论性评论。探索和列举了对研究界有益的各种资源，即期刊和数据集。最后，提出了一种基于未来强化学习的模型。在目前的工作中比较和评估了基于不一致的检测。该评论传达了用于评论的方法，分析了文献，最后是结论性评论。探索和列举了对研究界有益的各种资源，即期刊和数据集。最后，提出了一种基于未来强化学习的模型。在目前的工作中比较和评估了基于不一致的检测。该评论传达了用于评论的方法，分析了文献，最后是结论性评论。探索和列举了对研究界有益的各种资源，即期刊和数据集。最后，提出了一种基于未来强化学习的模型。