Steganalysis of Adaptive JPEG Steganography Based on ResDet

With the development of the adaptive JPEG steganography, steganalysis has become much more difficult in recent years. In order to detect the adaptive JPEG steganography, a CNN based framework, i.e. the ResDet, is proposed in this paper, which is sensitive to the artifacts caused by the adaptive JPEG steganography. To avoid the influences caused by various image content, the JPEG image under investigation is preprocessed by being passed through a series of filters. Then the feature maps are put into multiple convolutional layers. Contributing to the combination of the shortcut connection and the dense connection, the proposed network can differentiate JPEG steganography artifacts accurately with much more compact feature. Experiment results on Boss-Base with J-UNIWARD have demonstrated that the proposed framework with 84-dimensional feature, which will remarkably improve the efficiency of steganalysis, outperforms several state-of-the-art approaches investigated.

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