Improving Anticompression Robustness of JPEG Adaptive Steganography Based on Robustness Measurement and DCT Block Selection

With the development of the Internet, social network platforms (SNPs) have become the most common channel for image sharing. As a result, transmitting stego images in the public channels gives steganographers the best opportunity to transmit secret messages with behavioral security preserved. However, the SNPs typically compress uploaded images and damage the weak signal of steganography. In this study, a robust JPEG steganographic scheme based on robustness measurement and cover block selection (CBSRS) is proposed. We first design a deep learning-based model to fit the blockwise change rate of coefficients after JPEG recompression. Then, a cover block selection strategy is proposed to improve the robustness by optimizing the joint distortion function of transmission costs and classic costs. Moreover, by embedding indicator of cover block selection in chrominance channels of JPEG images, a shareable cover construction scheme is designed to solve the problem of auxiliary information transmission. The experimental results show that our proposed framework improves robustness while maintaining statistical security. Comparing with state-of-the-art methods, the framework achieves better performance under given recompression channels.

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.

Cryptographic Secrecy Analysis of Adaptive Steganographic Syndrome-Trellis Codes

Compared with traditional steganography, adaptive steganography based on STC (Syndrome-Trellis Codes) has extremely high antidetection ability and has been a mainstream and hot research direction in the field of information hiding over the past decades. However, it is noted, in specific scenarios, that a small number of methods can extract data from STC-based adaptive steganography, indicating security risks related to such algorithms. In this manuscript, the cryptographic secrecy of this kind of steganography is analyzed, on condition of two common attacks: stego-only attack and known-cover attack, respectively, from three perspectives: steganographic key equivocation, message equivocation, and unicity distance of the steganographic key. Focusing on the special layout characteristics of the parity-check matrix of STC, under the two attack conditions, the theoretical boundaries of the steganographic key equivocation function, the message equivocation function, and the unicity distance of the steganographic key are separately obtained, showing the impact of the three elements: the submatrix size, the randomness of the data, and the cover object on the cryptographic secrecy of the STC-based adaptive steganography, resulting in a theoretical reference to accurately judge the cryptographic secrecy of such steganography and design more secure steganography methods.

An approach for Secured Image Transmission: A High Capacitive and Confidentiality based Image Steganography using Private Stego-Key

In the proposed paper an approach for image transmission with security and also improvement of the gray-scale (8-bit image) image flexible stenographic system using LSB approach. In this process a secret key of 80 bits is applied while embedding the message into the cover image. To provide high security and also confidentiality of the data a key stego-key is applied. The proposed method the information bits are embedded adaptively into the cover-image pixels. With this method a high embedding capacity in terms of hiding the data is provided and also better imperceptibility is also achieved. The major advantage of this method verifies by the Security method of Digital Signature. It is to be verified whether the attacker has made a trials to change the Secret information in the present inside the stego-image which is intended to be kept secret throughout the communication process. In this technique the embedding process to hide the message data present in the transformed spatial domain of the cover image and makes use of a simple Exclusive-OR operation based on Security checking method of verifying the signature digitally by using key size value of 140 bits is used to check the integrity from the stego-image. The confidential data which is embedded can be retrieved from stego-images. The security level is enhanced by using the stego key and by adaptive steganography data inconspicuousness is improved.