Abstract

The authenticity and integrity of digital images is a critical and challenging research problem. Powerful image editing tools like Adobe Photoshop and PaintShop Pro enable the creation of highly convincing forged or tampered images for various malicious purposes. Analyzing and reliably distinguishing tampered images from authentic originals is an extremely difficult task due to the complex nature of images and the sophistication of modern tampering techniques. This paper presents a comprehensive survey and overview of current state-of-the-art methods for precisely localizing and detecting tampering in digital images through comparative analysis and image authentication mechanisms.

A wide range of approaches are covered, including fragile watermarking schemes that embed imperceptible data for tamper detection, feature extraction and machine learning classifiers to identify statistical inconsistencies, double compression artifact analysis for JPEG images, geometric and photometric inconsistency detection, and more. The principles, algorithms, advantages and limitations of each major technique are discussed in detail. Particular focus is given to recent developments in using robust hashing, human perceptual models, invariant feature point matching, and block-level tampering localization through selective authentication data embedding.

The paper aims to provide an in-depth yet accessible technical review that can serve as a reference for researchers working on trustworthy image forensics and authentication. Challenges, open problems and promising directions for future research in this field are also highlighted. The ultimate goal is to advance toward more accurate, comprehensive and efficient algorithms capable of reliably determining image integrity and precisely localizing any malicious tampering, thereby enhancing trust in digital image sources.

^{1Dr. P.L. Verma 2Dr. Shivendra Kumar Dwivedi1}