Time-efficient spliced image analysis using higher-order statistics
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ORIGINAL PAPER
Time‑efficient spliced image analysis using higher‑order statistics Ankit Kumar Jaiswal1 · Rajeev Srivastava1 Received: 23 January 2020 / Revised: 26 May 2020 / Accepted: 17 July 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Image forgery is gaining huge momentum as changing the content is no longer arduous. One of the leading techniques of this category is image splicing. This technique generates a composite image formed by combining regions of images. Once the image is forged, it becomes nearly impossible for the human expert to substantiate. Hence, for detecting and localizing the spliced region in the forged image, a tool is to be developed which has become the need of the hour. Articles have been reported that one of the key ingredients for such a tool is noise inconsistency, among others. The spliced region contains the non-homogeneous distribution of noise which acts as a feature to localize it. State-of-the-art techniques based on inconsistent noise are suffering from challenges like the requirement of prior knowledge about the image, localization of spliced region and estimation of inconsistent non-gaussian noise. In this paper, a blind local noise estimation technique has been introduced using a fourth-order central moment to localize the spliced region. This paper tries to overcome the challenges of state-of-the-art techniques. Experimental analysis has been done on images of three publicly available datasets. The results are evaluated on pixel level using confusion matrix and some other performance measures. The result of the given approach is compared with previously reported techniques and found better than them. Keywords Digital image forgery · Noise distribution · Fourth-order statistic · Time-efficient
1 Introduction Information and antiquity of images are scooped out in the subdiscipline area of forensic science i.e., digital image forensics. Analysis and interpretation of digital image content, metadata and legal matters related to digital images generally fall under the category of digital image forensics. Exploration of these contents can be used in multiple areas like content analysis, image authentication, photographic comparison and photogrammetry. Image authentication is the most important subdomain where it is being verified that a non-tampered image is presented in the correct framework or not and ensures that the image content is the same as it was at the time of acquisition. Digital image forgeries are generally inconspicuous though they leave statistical * Ankit Kumar Jaiswal [email protected] Rajeev Srivastava [email protected] 1
Computing and Vision Lab, Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh 221005, India
footprints which results in imperfect and unbalanced statistical properties of images. The major task is to recognize such variations in statistical properties so that tampering in images can be detected and localized. Manipulation of
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