Yongjun Kong

Multi-agent reversible data hiding (MAS-RDH) is a transformative paradigm for opinion network security, yet its optimization potential remains largely unexplored. This paper presents STORM-X, a self-contained exposition of the STORM five-layer architecture (Perception–Processing–Hiding–Control–Recovery) with holographic-channel embedding, and four experimentally validated optimization pathways. We provide theoretical justifications for the five-layer design grounded in defense-in-depth principles and for the SVD-based channel construction via unitary invariance and Weyl’s perturbation theorem. Six experimental studies validate component contributions through ablation, characterize capacity boundaries (K = 8–256), noise robustness (σ = 0–3.0), attack resilience (7 types), opinion dynamics (3 distributions), multi-agent scalability, and computational complexity. We further supplement the complexity analysis with detailed memory consumption metrics during the caching phase and provide exact wall-clock latency formulas for the embedding phase. A truncated SVD analysis establishes irreducibility, motivating precomputation caching. Results demonstrate BER = 0 for K ≤ 64 and σ ≤ 3.0, 5.71× multi-agent speedup, and 2,500× throughput improvement via caching. STORM-X is the first MAS-RDH framework simultaneously achieving perfect reversibility, real-time throughput, statistical undetectability (KS p > 0.05 at K ≤ 16), and tunable distortion control.

In the current distributed environment, reversible data hiding in encrypted images has the disadvantages of low security and nonprogressivity. To address this problem, a homomorphic embedding algorithm is proposed based on polynomial secret sharing (PSS) and Chinese remainder theorem. First, the image owner encrypts the carrier image in streaming encryption and sends it to the data hider. Then, the data hider utilizes PSS to split the carrier image into n shares. At the same time, extra secrets after SS are embedded into the carrier shares using homomorphism. After splitting by Chinese remainder theorem, every share of the embedded data is divided into some sub-shares and then distributed to the participants. The participants that satisfy the threshold condition provide part or all of the sub-shares according to the authority of the data extractor. If each participant provides all sub-shares, the secrets and carrier image can be reconstructed completely. If each participant provides part of the sub-shares, the secrets and carrier image can be reconstructed partly. The experimental results show that the proposed scheme has progressivity, high security, and a large embedding rate (ER). Meanwhile, the ER is not affected by the carrier image.

Reversible data hiding in encrypted domain takes encrypted data as the carrier to be embedded, which can embed secret information for lossless transmission on the basis of protecting data content. The reversibility of this technology can ensure that the original data can be recovered lossless after the secret information is extracted, which has important research significance for sensitive carrier data fields such as military, medical and judicial forensics. To better carry out related research of reversible data hiding in encrypted domain, different embedding frameworks of this technology are summarized in this paper.

McEliece cryptosystem is expected to be the next generation of the cryptographic algorithm due to its ability to resist quantum computing attacks. Few research studies have combined it with reversible data hiding in the encrypted domain (RDH-ED). In this article, we analysed and proved that there is a redundancy in the McEliece encryption process that is suitable for embedding. Then, a noise modulation-based scheme is proposed, called NM-RDHED, which is suitable for any signal and not only for images. The content owner scrambles the original image and then encrypts it with the receiver’s public key. The data hider generates a load noise by modulating additional data. After that, the load noise is added to the encrypted image, which achieves the data embedding. The reconstructed image is without any distortion after the direct decryption of the marked image, and the extracted data are no errors. The experimental results demonstrate our scheme has a higher embedding rate and more security, which is superior to existing schemes.

Considering the correlation existing among different color channels of the image, we propose a novel reversible data hiding (RDH) scheme based on multichannel difference value ordering. By applying the technique of pixel value ordering (PVO), we establish a certain relationship by sorting the differences among adjacent pixels of different color channels. Combined with the optimized pairwise prediction-error expansion by analyzing the distribution of pixel difference, our scheme can adaptively select pixel channel to achieve reversible embedding of information. Through simulation experiments, the feasibility of this RDH scheme in image distortion and computational complexity is verified. When the threshold T is set to 3, the average PSNR of the test images embedded with 5000bits of additional information in each color channel can reach 63.41, 64.38, and 63.96, respectively. When the embedding capacity is 20000bits, the PSNR of the color image can reach 58.90dB. Considering the embedding capacity and image distortion comprehensively from the simulation data, our proposed scheme has a better performance than the previous PVO-RDH schemes which only consider a single channel.

The existing reversible data hiding methods in encrypted domain separate image encryption from information embedding and do not make full use of the redundant space in the process of image encryption. In order to improve the performance of reversible data hiding by using the technical characteristics of image encryption, a reversible data hiding method based on McEliece error correction is proposed. Firstly, the segmentation position of bit plane is determined according to the embedding requirement and texture characteristic, and the image is divided into high and low significant bits. Secondly, because of the error-correcting characteristic of McEliece encryption, reversible data embedding can be realized while encrypting low significant bits. Then, the high significant bits are compressed to reserve space for the ciphertext extension of the low significant bits. Finally, the whole high significant bits information is stream-encrypted. As long as the image receiver has the decryption key, the image can be restored without distortion. By concealing the relationship between error correction and secret information mapping, the concealment of secret information transmission can be realized. In addition, due to different processing for different pixels, it can be efficiently transmitted with low computational complexity for applications that only need general images. The simulation results show that this scheme can not only realize the separable operation of information extraction and image recovery but also resist the noise attack to a certain extent. The maximum embedding rates of 10 standard images from USC-SIPI and 50 standard images from BOSS-BASE are 2.228 and 2.323 bpp, respectively.

With the continuous improvement in the accuracy of steganalysis based on convolutional neural networks (CNNs), the network scale has shown explosive growth. Consequently, CNNs have a high demand for hardware resources and time-consuming training. To reduce the number of CNN parameters and improve the efficiency of steganalysis, we propose a lightweight steganalysis CNN called W-Net. The proposed W-Net first uses grouped convolution and channel shuffling units to extract noise residuals, strengthen the information exchange between groups, and improve feature extraction. In addition, the depth-wise separable convolution is applied to obtain different channels and spaces. The fusion of position information achieves the effect of conventional convolution while reducing the number of network parameters. We verified the effect of activation functions on steganalysis accuracy through experiments. In addition, the proposed W-Net can detect the steganographic data from the S-UNIWARAD spatial steganography algorithm with an embedding rate of 0.4 bpp. Compared with Xu-Net and Zhu-Net, the proposed W-Net improves the detection accuracy by 12.70% and 6.38%, respectively.