Chengyu Mo^1,2, Fenlin Liu^1,2, Ma Zhu^1,2,*, Gengcong Yan³, Baojun Qi^1,2, Chunfang Yang^1,2

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2921-2936, 2023, DOI:10.32604/cmc.2023.039540 - 08 October 2023

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… More >

Yuchen Li¹, Baohong Ling^1,2,*, Donghui Hu¹, Shuli Zheng¹, Guoan Zhang³

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2213-2225, 2023, DOI:10.32604/iasc.2023.029983 - 21 June 2023

Abstract The goal of steganalysis is to detect whether the cover carries the secret information which is embedded by steganographic algorithms. The traditional steganalysis detector is trained on the stego images created by a certain type of steganographic algorithm, whose detection performance drops rapidly when it is applied to detect another type of steganographic algorithm. This phenomenon is called as steganographic algorithm mismatch in steganalysis. To resolve this problem, we propose a deep learning driven feature-based approach. An advanced steganalysis neural network is used to extract steganographic features, different pairs of training images embedded with steganographic More >

Rukiye Karakis^1,2,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4649-4666, 2023, DOI:10.32604/cmc.2023.035881 - 28 December 2022

Abstract Medical image steganography aims to increase data security by concealing patient-personal information as well as diagnostic and therapeutic data in the spatial or frequency domain of radiological images. On the other hand, the discipline of image steganalysis generally provides a classification based on whether an image has hidden data or not. Inspired by previous studies on image steganalysis, this study proposes a deep ensemble learning model for medical image steganalysis to detect malicious hidden data in medical images and develop medical image steganography methods aimed at securing personal information. With this purpose in mind, a… More >

Yuwei Chen^{1, 2}, Yuling Chen^{1, *}, Yu Yang^{1, 2}, Xinda Hao², Ning Wang²

CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1259-1271, 2020, DOI:10.32604/cmc.2020.06723

Abstract Local binary pattern (LBP) is one of the most advanced image classification recognition operators and is commonly used in texture detection area. Research indicates that LBP also has a good application prospect in steganalysis. However, the existing LBP-based steganalysis algorithms are only capable to detect the least significant bit (LSB) and the least significant bit matching (LSBM) algorithms. To solve this problem, this paper proposes a steganalysis model called msdeLTP, which is based on multi-scale local ternary patterns (LTP) and derivative filters. The main characteristics of the msdeLTP are as follows: First, to reduce the… More >

Yuhan Kang¹, Fenlin Liu¹, Chunfang Yang^1,*, Xiangyang Luo¹, Tingting Zhang²

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 315-329, 2019, DOI:10.32604/cmc.2019.05242

Abstract This study proposes a color image steganalysis algorithm that extracts high-dimensional rich model features from the residuals of channel differences. First, the advantages of features extracted from channel differences are analyzed, and it shown that features extracted in this manner should be able to detect color stego images more effectively. A steganalysis feature extraction method based on channel differences is then proposed, and used to improve two types of typical color image steganalysis features. The improved features are combined with existing color image steganalysis features, and the ensemble classifiers are trained to detect color stego More >

Yu Yang^1,2,*, Yuwei Chen^1,2, Yuling Chen², Wei Bi^3,4

CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 261-272, 2018, DOI:10.3970/cmc.2018.02736

Abstract The state-of-the-art universal steganalysis method, spatial rich model (SRM), and the steganalysis method using image quality metrics (IQM) are both based on image residuals, while they use 34671 and 10 features respectively. This paper proposes a novel steganalysis scheme that combines their advantages in two ways. First, filters used in the IQM are designed according to the models of the SRM owning to their strong abilities for detecting the content adaptive steganographic methods. In addition, a total variant (TV) filter is also used due to its good performance of preserving image edge properties during filtering.… More >

Junjia Chen¹, Wei Lu^1,2,*, Yuileong Yeung¹, Yingjie Xue¹, Xianjin Liu¹, Cong Lin^1,3, Yue Zhang⁴

CMC-Computers, Materials & Continua, Vol.55, No.2, pp. 201-211, 2018, DOI:10.3970/cmc.2018.01781

Abstract In recent years, binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information security. In most state-of-the-art binary image steganographic schemes, they always find out the flippable pixels to minimize the embedding distortions. For this reason, the stego images generated by the previous schemes maintain visual quality and it is hard for steganalyzer to capture the embedding trace in spacial domain. However, the distortion maps can be calculated for cover and stego images and the difference between them is significant. In this paper, a novel binary More >