A Content-Adaptive Median Filtering Detection Using Markov Transition Probability Matrix of Pixel Intensity Residuals. Issue 1 (2nd January 2019)

• Record Type: Journal Article
• Authors: Agarwal, Saurabh; Chand, Satish
• Is Part Of: Journal of applied security research. Volume 14:Issue 1(2019)
• Page Start: 88
• Page End: 105
• Publication Date: 2019-01-02
• Subjects: Anti-forensics -- forensic science -- image forensic -- image forgery -- image tampering -- Markov process -- median filtering detection; Police, Private -- Training of -- Periodicals; Police, Private -- Training of -- United States -- Periodicals; Private security services -- Periodicals; 363.289
• DOI: 10.1080/19361610.2019.1545274
• Languages: English
• ISSNs: 1936-1610

Abstract

Abstract: Excessive dependence on digital images has raised the need for its forensic analysis. Median filtering, due to its nonlinear nature, is extensively used in image antiforensics to hide the evidence of image forgery. There exist several methods based on the Markov transition probability matrix to detect median filtering. In this article, to detect median filtering, a content-adaptive thresholding approach is applied on pixel intensity residuals and features are extracted using a Markov transition probability matrix. This modified approach provides better results even on low-resolution and highly compressed images in comparison to existing methods.