Tampering Detection and Content Authentication Using Encrypted Perceptual Hash for Image Database

To detect and locate unapproved alterations or modifications made to digital pictures, tampering detection in images is a crucial study topic in the field of image forensics. As sophisticated picture altering tools become more widely available, it has become more difficult to guarantee the integrity and authenticity of digital visual output. This overview of the literature explores methodology, findings, and contributions in key tampering detection research studies. The breakthroughs in deep learning techniques and steganalysis are highlighted together with more conventional approaches like statistical analysis and error level analysis. The difficulties tampering detection faces are discussed, including generalisation, dataset accessibility, resilience against adversarial assaults, computing effectiveness, and moral and legal issues. With the goal of advancing the creation of precise, reliable, and effective algorithms for maintaining the integrity and authenticity of digital pictures, the paper offers insights into the current state of the art, limits, and future research paths in tampering detection.