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Abstract
This paper presents an algorithm for detecting one of the most commonly used types of digital image forgeries - splicing. The algorithm is based on the use of the VGG-16 convolutional neural network. The proposed network architecture takes image patches as input and obtains classification results for a patch: original or forgery. On the training stage we select patches from original image regions and on the borders of embedded splicing. The obtained results demonstrate high classification accuracy (97.8% accuracy for fine-tuned model and 96.4% accuracy for the zero-stage trained) for a set of images containing artificial distortions in comparison with existing solutions. Experimental research was conducted using CASIA dataset.
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1 Samara National Research University, Moskovskoe Shosse 34A, Samara, Russia, 443086; Image Processing Systems Institute of RAS - Branch of the FSRC “Crystallography and Photonics” RAS, Molodogvardejskaya street 151, Samara, Russia, 443001