Search

The use of the digital images in modern society is very common, making it difficult to name an industry where digital images are not used. However, in certain situations, the digital images may be direct or indirect digital evidence, such as in court cases. Tampering or violating the integrity of the digital images is one of the important issues in the information protection. The issue of the digital signal authentication by cybersecurity experts is a pressing issue in the present days. Therefore, the development of methods and algorithms for detecting the integrity of digital signals in general, and digital images in particular, is a very crucial task. Among all the possible variations in the integrity violation of digital images can be distinguished a separate group, in which the digital images are processed by various filters editors, such as blurring, sharpening, etc. The blurring is often applied to images not only when they are tampered, but also as a steganographic attack. Blur detection indicates the inability to use a digital image as the evidence of anything. One of the most effective blur detection methods known from the open sources is a method based on the analysis of singular values of blocks of a digital image matrix. The aim of this paper is to increase the efficiency of the blur detection in a digital image by modifying an algorithm based on the analysis of singular values. Computational experiments are performed to determine the number of parameters to be verified. The results obtained allow modifying the algorithm of the method of the blur detection in a digital image. The modification makes it possible to detect tampered access to a digital image. The modified algorithm is no less effective than the original algorithm, and allows analyzing fewer parameters in a shorter time. The computational complexity of the algorithm is determined by the second order polynomial that is acceptable in terms of the practical application. [ABSTRACT FROM AUTHOR]

The information technologies are spreading in the modern world and are one or more interconnected software products for a particular computer that helps the user achieve this aim. Every day we receive a wealth of digital information, such as emails, articles on the Internet, messages in various communication systems, and more. Therefore, the issue of qualitative and rapid verification of its authenticity, namely, elements such as digital images, is very timeliness. Digital images are often not original. They have different types of integrity violations, such as cloning or collage. After applying these operations, the different filters for blurring, brightness and contrast variation, sharpening, etc. often are used to hide the digital image processing .These operations are performed by graphic editors such as GIMP. In this graphic editor, which is a free analogue to the Adobe Photoshop graphic editor, sharpening is implemented as an Unsharp Mask Filter. A review of open source literature has shown that little attention is paid to detecting artificial sharpening. From open sources it is known about the method for detection of artificial sharpening based on the analysis of close color pairs of the image matrix. Its main disadvantage is the high number of the type I errors and the type II errors. This fact leads to the development of new methods and algorithms for detection this type of image processing. The object of this work is to detect artificial sharpening of a digital image as an integrity violation. A study is conducted in which the formal parameters of the digital image matrix were revealed, indicating the presence of artificial sharpening. A quantitative estimation of the qualitative differences between processed and raw images is obtained and used as a threshold for detecting artificially sharpened images. An algorithm for the detection of the artificial sharpening of a digital image is developed and its efficiency is estimated, According to this algorithm, the number of the type I errors is 3,8%, and the number of the type II errors is 9,8%. The development potential of this work is to improve the detection of different ways of artificial sharpening the digital image. [ABSTRACT FROM AUTHOR]