Forensic Technique for Forgery Detection and Localization in Digital Image

Abstract: The widespread availability of advanced image editing software has transformed digital image forgery into an urgent issue in multimedia forensics. Traditional forgery methods—copy-move, splicing, and retouching—taint the authenticity of digital images, allowing malicious individuals to disseminate misinformation, tamper with legal evidence, and compromise digital trust. Forgery detection and localization are crucial for uses in cybersecurity, journalism, law enforcement, and digital forensics.This paper provides a systematic survey and comparative evaluation of the latest forensic methods for detecting forgery and localizing it. We classify existing methods intoDigital image forensics is concerned with confirming image genuineness by identifying evidence of tampering. Typical forgery methods are:Copy-Move Forgery (CMF): Copying and pasting areas within the same image.Image Splicing: Merging pieces from multiple images into a composite.Retouching: Modifying image characteristics (e.g., eliminating objects or altering face features).Handcrafted feature-based traditional techniques (e.g., DCT coefficients, SIFT keypoints, noise discrepancies, and JPEG compression artifact). Deep learning-based methods based on CNNs, autoencoders, and GANs to identify covert tampering signals.