Chengyu Mo1,2, Fenlin Liu1,2, Ma Zhu1,2,*, Gengcong Yan3, Baojun Qi1,2, Chunfang Yang1,2
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2921-2936, 2023, DOI:10.32604/cmc.2023.039540
Abstract The training images with obviously different contents to the detected images will make the steganalysis model perform poorly in deep steganalysis. The existing methods try to reduce this effect by discarding some features related to image contents. Inevitably, this should lose much helpful information and cause low detection accuracy. This paper proposes an image steganalysis method based on deep content features clustering to solve this problem. Firstly, the wavelet transform is used to remove the high-frequency noise of the image, and the deep convolutional neural network is used to extract the content features of the low-frequency information of the image.… More >
Login
Register
