A Novel High-Capacity Information Hiding Scheme Based on Improved U-Net

With the gradual introduction of deep learning into the field of information hiding, the capacity of information hiding has been greatly improved. Therefore, a solution with a higher capacity and a good visual effect had become the current research goal. A novel high-capacity information hiding scheme based on improved U-Net was proposed in this paper, which combined improved U-Net network and multiscale image analysis to carry out high-capacity information hiding. The proposed improved U-Net structure had a smaller network scale and could be used in both information hiding and information extraction. In the information hiding network, the secret image was decomposed into wavelet components through wavelet transform, and the wavelet components were hidden into image. In the extraction network, the features of the hidden image were extracted into four components, and the extracted secret image was obtained. Both the hiding network and the extraction network of this scheme used the improved U-Net structure, which preserved the details of the carrier image and the secret image to the greatest extent. The simulation experiment had shown that the capacity of this scheme was greatly improved than that of the traditional scheme, and the visual effect was good. And compared with the existing similar solution, the network size has been reduced by nearly 60%, and the processing speed has been increased by 20%. The image effect after hiding the information was improved, and the PSNR between the secret image and the extracted image was improved by 6.3 dB.

Optimal Image Based Information Hiding with One-dimensional Chaotic Systems and Dynamic Programming

Information hiding is a technology aimed at the secure hiding of important information into digital documents or media. In this paper, a new approach is proposed for the secure hiding of information into gray scale images. The hiding is performed in two stages. In the first stage, the binary bits in the sequence of information are shuffled and encoded with a set of integer keys and a system of one-dimensional logistic mappings. In the second stage, the resulting sequence is embedded into the gray values of selected pixels in the given image. A dynamic programming method is utilized to select the pixels that minimize the difference between a cover image and the corresponding stego image. Experiments show that this approach outperforms other information hiding methods by 13.1% in Peak Signal to Noise Ratio (PSNR) on average and reduces the difference between a stego image and its cover image to 0 in some cases.

INDIRECT INFORMATION HIDING TECHNOLOGY ON A MULTIADIC BASIS

It is shown that the current direction of increasing the safety of information resources when transmitting information in info-communication systems is the use of methods of steganographic instruction in video imagery. The effectiveness of such methods is significantly increased when used in a complex of methods of concealment, which are based on the principles of inconsistent and cosmic communication. At the same time, existing methods of steganographic are used in the process of insertion of information mainly only laws, empty features of visual perception of video images. So, it is justified that the scientific and applied problem, which is to increase the density of embedded messages in the video container with a given level of their reliability, is relevant. The solution of this problem is based on the solution of the contradiction, which concerns the fact that increasing the density of embedded data leads to a decrease in the bit rate of the video container, steganalysis stability, reliability of special information, and video container. Therefore, the research aims to develop a methodology for the steganographic embedding of information, taking into account the regularities of the video container, which are generated by its structural and structural-statistical features. The solution to the posed problem of applying steganographic transformations is proposed to be realised by methods of indirectly embedding parts of the hidden message in certain conditions or functional relationships. The possibility of creating steganographic transformations regarding the indirect embedding and extraction of hidden information in a multiadic basis by modifying the underlying basis system within an admissible set is demonstrated. It is shown that the multiadic system, which is created in the spectral space of DCT transforms, has the potential to form a set of admissible modifications of basis systems.

A Two-domain Data Hiding Scheme for Data Signature in Integrated Online Services Applications

There is confidential data in integrated online services applications. The data needs to be hidden before transmission on the Internet, and the data authenticity should be assured. Information hiding technology is an effective way of protecting the confidential data and may be used for assuring data authenticity. A Two-domain data hiding scheme (TDHS) is introduced in the paper to protect contract document authenticity in grid system. TDHS aims to embed confidential data into signature image in both spatial and frequency domain. LSB-like algorithm is used to generate a signature image Hs by embedding feature data of contract document into the original signature image H in the spatial domain. Then DCT algorithm is adopted to embed the watermark in signature image Hs in the frequency domain, and generate signature image Hs ready . The signature image Hs ready is inserted into the contract document. When received the document with signature, the recipient can verify its authenticity. The feasibility of TDHS is verified through experiments.

Dataset Generation for Development of Multi-Node Cyber Threat Detection Systems

This paper presents a new approach to generate datasets for cyber threat research in a multi-node system. For this purpose, the proof-of-concept of such a system is implemented. The system will be used to collect unique datasets with examples of information hiding techniques. These techniques are not present in publicly available cyber threat detection datasets, while the cyber threats that use them represent an emerging cyber defense challenge worldwide. The network data were collected thanks to the development of a dedicated application that automatically generates random network configurations and runs scenarios of information hiding techniques. The generated datasets were used in the data-driven research workflow for cyber threat detection, including the generation of data representations (network flows), feature selection based on correlations, data augmentation of training datasets, and preparation of machine learning classifiers based on Random Forest and Multilayer Perceptron architectures. The presented results show the usefulness and correctness of the design process to detect information hiding techniques. The challenges and research directions to detect cyber deception methods are discussed in general in the paper.