An image authentication technology based on depth residual network

Abstract

The traditional image authentication technique generally determines the image attribution by extracting specific features and combining the similarity calculation algorithm. Because of the selected features dimensions, characterization and other factors, the accuracy and speed of image authentication have been restricted. In this paper, Recog-Net, an end-to-end image authentication model based on convolution neural network has been proposed. Deep residual network is chosen as the features extractor. Mahalanobis distance and threshold method are used to complete the image authentication. Experiments show that the performance of the extractor’s features, compared with the traditional features and the features of other convolution neural network architectures, is more excellent, with a high degree of generality, recognition rate and robustness, still having these advantages even after a substantial compression. The Recog-Net for image authentication is able to accurately authenticate the images tampered with certain range.