Blind Integrity Verification of Medical Images


This paper presents the first method of digital blind forensics among the medical imaging field with the target to detect whether an image has been changed by some processing (e.g., filtering, lossy compression, and so on). It compares 2 image options: the histogram statistics of reorganized block-primarily based discrete cosine rework coefficients, originally proposed for steganalysis functions, and also the histogram statistics of reorganized block-primarily based Tchebichef moments. Each options function input of a collection of support vector machine classifiers built so as to discriminate tampered pictures from original ones as well as to identify the character of the worldwide modification one image may have undergone. Performance analysis, conducted in application to totally different medical image modalities, shows that these image options will help, independently or jointly, to blindly distinguish image processing or modifications with a detection rate larger than 70p.c. They additionally underline the complementarity of these features.

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