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