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Editor-in-Chief
Nikiforov
Vladimir O.
D.Sc., Prof.
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doi: 10.17586/2226-1494-2020-20-3-425-431
U-NET ARCHITECTURE NEURAL NETWORK FOR LOCALIZATION OF DIGITAL IMAGES INTEGRITY VIOLATION
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Article in Russian
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Abstract
For citation:
Abdullina K.M., Spivak A.I. U-Net architecture neural network for localization of digital images integrity violation. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2020, vol. 20, no. 3, pp. 425–431 (in Russian). doi: 10.17586/2226-1494-2020-20-3-425-431
Abstract
Subject of Research. The paper presents the study of the U-Net architecture neural network applicability to localization problem of image modifications. The implemented method provides the detecting of the modified image and getting a mask of the changed area. Method. The proposed method was based on deep machine learning – a neural network. U-Net neural network architecture was studied. The training dataset was created as a basis for model training with original images and images modified using a graphical editor. The implemented method represents image as a set of pixels. Main Results. The trained model has shown a high level of brightness recognition for image modifications, up to 80 %, and up to 64 % for copy-shift. Practical Relevance. The result can be practically applicable in the forensics for recognition of modified image blocks and for copyright protection.
Keywords: images, modification, neural networks, U-Net, information security, integrity violation
References
References
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