doi: 10.17586/2226-1494-2018-18-2-299-306


A. I. Serova, A. I. Spivak

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Article in Russian

For citation: Serova A.I., Spivak A.I. Application of maсhine learning methods for detecting of JPEG image integrity violations. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2018, vol. 18, no. 2, pp. 299–306 (in Russian). doi: 10.17586/2226-1494-2018-18-2-299-306


Subject of Research. The paper presents the study on the JPEG image integrity violations and existing methods of their detection. We propose a method for detection of modified image and the source of its modification. The method gives the possibility to determine the original image and camera model that recorded it. Method. The method was developed with the use of machine learning tools. The following machine learning methods have been studied: naive Bayesian classifier, decision tree, logistic regression, k-nearest neighbors, SVC, random forest. The base for model training was formed by the original photos from website www.steves-digicams.comthat were modified by different graphic editors. The proposed method uses JPEG-image structure in byte view, namely, markers. Availability of markers and their number were suggested as classification features. Main Results. The trained model has demonstrated high classification result equal to more than 95%. Among all evaluated algorithms the two ones have shown the best results: decision tree and random forest. Decision tree was chosen as the best one upon stability criterion. Practical Relevance. Thereceived result can be practically applicable in the area of forensics and information security.

Keywords: machine learning, images, integrity, information security, JPEG

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