Disguise of Steganography Behaviour: Steganography Using Image Processing with Generative Adversarial Network

The deep learning based image steganalysis is becoming a serious threat to modification-based image steganography in recent years. Generation-based steganography directly produces stego images with secret data and can resist the advanced steganalysis algorithms. This paper proposes a novel generation-based steganography method by disguising the stego images into the kinds of images processed by normal operations (e.g., histogram equalization and sharpening). Firstly, an image processing model is trained using DCGAN and WGAN-GP, which is used to generate the images processed by normal operations. Then, the noise mapped by secret data is inputted into the trained model, and the obtained stego image is indistinguishable from the processed image. In this way, the steganographic process can be covered by the process of image processing, leaving little embedding trace in the process of steganography. As a result, the security of steganography is guaranteed. Experimental results show that the proposed scheme has better security performance than the existing steganographic methods when checked by state-of-the-art steganalytic tools, and the superiority and applicability of the proposed work are shown.

Feature-guided Deep Subdomain Adaptation Network for Dataset Mismatch in Spatial Steganalysis

Steganalysis aims to detect covert communication established via steganography. In recent years, numerous deep learning-based image steganalysis methods with high performance have been proposed. However, these methods tend to suffer from distinct performance degradation when cover images in the train and test set are quite different, also known as cover source mismatch. To address this limitation, in this paper, a feature-guided deep subdomain adaptation network is proposed. Initially, the predictions of the pretrained model are used as pseudo labels to divide the unlabeled samples of the target domain into different subdomains, and the distributions of the relevant subdomains are aligned by subdomain adaptation. Afterwards, since the steganalysis model may assign incorrect predictions to samples in the target domain, we integrate guiding features to make the division of subdomains more precise. The experimental results show that the proposed network is significantly better than other three networks such as Steganalysis Residual Network (SRNet), deep adaptive network (J-Net) and Deep Subdomain Adaptation Network (DSAN), when it is used to detect three spatial steganographic algorithms with a wide variety of datasets and payloads. Especially, compared with SRNet, the average accuracy of our method is increased by 5.4% at 0.4bpp and 8.5% at 0.2bpp in the case of dataset mismatch.

Forensic steganalysis for identification of steganography software tools using multiple format image

<p>Today many steganographic software tools are freely available on the Internet, which helps even callow users to have covert communication through digital images. Targeted structural image steganalysers identify only a particular steganographic software tool by tracing the unique fingerprint left in the stego images by the steganographic process. Image steganalysis proves to be a tough challenging task if the process is blind and universal, the secret payload is very less and the cover image is in lossless compression format. A payload independent universal steganalyser which identifies the steganographic software tools by exploiting the traces of artefacts left in the image and in its metadata for five different image formats is proposed. First, the artefacts in image metadata are identified and clustered to form distinct groups by extended K-means clustering. The group that is identical to the cover is further processed by extracting the artefacts in the image data. This is done by developing a signature of the steganographic software tool from its stego images. They are then matched for steganographic software tool identification. Thus, the steganalyser successfully identifies the stego images in five different image formats, out of which four are lossless, even for a payload of 1 byte. Its performance is also compared with the existing steganalyser software tool.</p>

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

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.

An Efficient Steganalysis of Medical Images by Using Deep Learning Based Discrete Scalable Alex Net Convolutionary Neural Networks Classifier

Steganalysis is the technique that tries to beat steganography by detecting and removing secret information. Steganalysis involves the detection of a message embedded in a picture. Deep Learning (DL) advances have offered alternative approaches to many difficult issues, including the field of image steganalysis using deep-learning architecture based on convolutionary neural networks (CNN). In recent years, many CNN architectures have been established that have enhanced the exact identification of steganographic images. This work presents a novel architecture which involves a preprocessing stage using histogram equalization and adaptive recursive median filter banks to reduce image noise, a feature extraction stage using shearlet multilinear local embedding methods and then finally the classification can be done by using the discrete scalable Alex NET CNN classifier. Performance was evaluated on the RGB-BMP Steganalysis Dataset with different experimental setups. To prove the effectiveness of the suggested algorithm it can be compared with the other existing methodologies. This work improves classification accuracies on all other existing algorithms over test data.