scholarly journals Color image steganalysis based on embedding change probabilities in differential channels

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772091782 ◽  
Author(s):  
Chunfang Yang ◽  
Yuhan Kang ◽  
Fenlin Liu ◽  
Xiaofeng Song ◽  
Jie Wang ◽  
...  
Keyword(s):  
Color Image ◽  
Adaptive Steganography ◽  
Potential Threat ◽  
Sensitive Data ◽  
Image Steganalysis ◽  
Feature Based ◽  
Occurrence Matrix ◽  
Model Features ◽  
Color Image Steganography ◽  
The Internet Of Things

It is a potential threat to persons and companies to reveal private or company-sensitive data through the Internet of Things by the color image steganography. The existing rich model features for color image steganalysis fail to utilize the fact that the content-adaptive steganography changes the pixels in complex textured regions with higher possibility. Therefore, this article proposes a variant of spatial rich model feature based on the embedding change probabilities in differential channels. The proposed feature is extracted from the residuals in the differential channels to reduce the image content information and enhance the stego signals significantly. Then, the embedding change probability of each element in the differential channels is added to the corresponding co-occurrence matrix bin to emphasize the interference of the residuals in textured regions to the improved co-occurrence matrix feature. The experimental results show that the proposed feature can significantly improve the detection performances for the WOW and S-UNIWARD steganography, especially when the payload size is small. For example, when the payload size is 0.05 bpp, the detection errors can be reduced respectively by 5.20% and 4.90% for WOW and S-UNIWARD by concatenating the proposed feature to the color rich model feature CRMQ1.

scholarly journals Color image steganalysis based on channel gradient correlation

2019 ◽  
Vol 15 (5) ◽  
pp. 155014771985203 ◽  
Author(s):  
Yuhan Kang ◽  
Fenlin Liu ◽  
Chunfang Yang ◽  
Lingyun Xiang ◽  
Xiangyang Luo ◽  
...  
Keyword(s):  
Color Image ◽  
Ensemble Classifier ◽  
Image Steganography ◽  
Geometric Transformation ◽  
Image Steganalysis ◽  
Different Color ◽  
Occurrence Matrix ◽  
Color Image Steganography ◽  
The Internet Of Things ◽  
Color Channels

It is one of the potential threats to the Internet of Things to reveal confidential messages by color image steganography. The existing color image steganalysis algorithm based on channel geometric transformation measures owns higher accuracy than the others, but it fails to utilize the correlation between the gradient amplitudes of different color channels. Therefore, this article points out that the color image steganography weakens the correlation between the gradient amplitudes of different color channels and proposes a color image steganalysis algorithm based on channel gradient correlation. The proposed algorithm extracts the co-occurrence matrix feature from the gradient amplitude residuals among different color channels and then combines it with the existing color image steganalysis features to train the ensemble classifier for color image steganalysis. The experimental results show that, for WOW and S-UNIWARD steganography, compared with the existing algorithms, the proposed algorithm outperforms the existing algorithms.

scholarly journals A Novel Technique for Image Steganalysis Based on Separable Convolution and Adversarial Mechanism

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2742
Author(s):  
Yuwei Ge ◽  
Tao Zhang ◽  
Haihua Liang ◽  
Qingfeng Jiang ◽  
Dan Wang
Keyword(s):  
Signal To Noise Ratio ◽  
Detection Performance ◽  
Hidden Information ◽  
Adaptive Steganography ◽  
Image Steganalysis ◽  
Channel Correlation ◽  
Cyberspace Security ◽  
And Training ◽  
Training Efficiency ◽  
Good Detection

Image steganalysis is a technique for detecting the presence of hidden information in images, which has profound significance for maintaining cyberspace security. In recent years, various deep steganalysis networks have been proposed in academia, and have achieved good detection performance. Although convolutional neural networks (CNNs) can effectively extract the features describing the image content, the difficulty lies in extracting the subtle features that describe the existence of hidden information. Considering this concern, this paper introduces separable convolution and adversarial mechanism, and proposes a new network structure that effectively solves the problem. The separable convolution maximizes the residual information by utilizing its channel correlation. The adversarial mechanism makes the generator extract more content features to mislead the discriminator, thus separating more steganographic features. We conducted experiments on BOSSBase1.01 and BOWS2 to detect various adaptive steganography algorithms. The experimental results demonstrate that our method extracts the steganographic features effectively. The separable convolution increases the signal-to-noise ratio, maximizes the channel correlation of residuals, and improves efficiency. The adversarial mechanism can separate more steganographic features, effectively improving the performance. Compared with the traditional steganalysis methods based on deep learning, our method shows obvious improvements in both detection performance and training efficiency.

scholarly journals A secure image steganography based on burrows wheeler transform and dynamic bit embedding

2019 ◽  
Vol 9 (1) ◽  
pp. 460
Author(s):  
Ahmed Toman Thahab
Keyword(s):  
Communication Networks ◽  
Color Image ◽  
Image Steganography ◽  
Digital Data ◽  
Embedding Capacity ◽  
Image Size ◽  
Secret Data ◽  
Exclusive Or ◽  
Burrows Wheeler Transform ◽  
Color Image Steganography

In modern public communication networks, digital data is massively transmitted through the internet with a high risk of data piracy. Steganography is a technique used to transmit data without arousing suspicion of secret data existence.  In this paper, a color image steganography technique is proposed in spatial domain. The cover image is segmented into non-overlapping blocks which are scattered among image size window using Burrows Wheeler transform before embedding. Secret data is embedded in each block according to its sequence in the Burrows Wheeler transform output. The hiding method is an operation of an exclusive-or between a virtual bit which is generated from the most significant bit and the least significant bits of the cover pixel. Results of the algorithm are analyzed according to its degradation of the output image and embedding capacity. The results are also compared with other existing methods.

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