Existing methods for reversible data hiding in ciphertext binary images only involve one data hider to perform data embedding. When the data hider is attacked, the original binary image cannot be perfectly reconstructed. To this end, this letter proposes multi-party reversible data hiding in ciphertext binary images, where multiple data hiders are involved in data embedding. In this solution, we use visual cryptography technology to encrypt a binary image into multiple ciphertext binary images, and transmit the ciphertext binary images to different data hiders. Each data hider can embed data into a ciphertext binary image and generate a marked ciphertext binary image. The original binary image is perfectly reconstructed by collecting a portion of marked ciphertext binary images from the unattacked data hiders. Compared with existing solutions, the proposed solution enhances the recoverability of the original binary image. Besides, the proposed solution maintains a stable embedding capacity for different categories of images.

Generative steganography stands as a promising technique for information hiding, primarily due to its remarkable resistance to steganalysis detection. Despite its potential, hiding a secret image using existing generative steganographic models remains a challenge, especially in lossy or noisy communication channels. This paper proposes a robust generative steganography model for hiding full-size image. It lies on three reversible concatenated mappings proposed. The first mapping uses VQGAN with an order-preserving codebook to compress an image into a more concise representation. The second mapping incorporates error correction to further convert the representation into a robust binary representation. The third mapping devises a distribution-preserving sampling mapping that transforms the binary representation into the latent representation. This latent representation is then used as input for a text-to-image Diffusion model, which generates the final stego image. Experimental results show that our proposed scheme can freely customize the stego image content. Moreover, it simultaneously attains high stego and recovery image quality, high robustness, and provable security.

Generative steganography is renowned for its exceptional undetectability. However, prevalent generative methods often have insufficient capacity for concealing secret images. Furthermore, the sensitivity of commonly utilized generative models exacerbates the challenge of ensuring robustness against channel distortions such as JPEG compression. In this paper, we introduce a generative image hiding network that employs two invertible generators to transform secret images into stego images within a disparate image domain. Additionally, we seamlessly integrate an up-and-down sampling module (UDM) within these generators to facilitate efficient decoupling of the intermediate representations obtained by each generator. The UDM serves multiple purposes: preserving coherence between the intermediate representations, enhancing resilience against JPEG compression, and safeguarding the confidentiality of the concealed images. To address the complexity of mapping both uncompressed and compressed stego images to a unified intermediary representation, we implement two distinct flows for the forward and backward processes of the generator associated with the stego images. The experimental results show that our scheme offers concurrent advantages in terms of full-size image hiding ability, undetectability, confidentiality, and robustness.

Capturing displayed images using portable cameras has become familiar among multimedia pirates, necessitating the urgent requirement of camera-shooting resilient watermarking schemes. In this paper, we consider the stealers who only record parts of images, and propose a robust watermarking scheme at the image instance level. This scheme consists of an encoding end, a noise layer, and a decoding end. The encoding end first selects specific watermarking regions associated with segmented image instances. Afterwards, an encoder is employed to embed watermark sequences into the RGB color model of these watermarking regions. At last, templates are embedded to product the final watermarked images. Specifically, our suggested template-based resynchronization comprises a template embedding module at the encoding end and a geometric correction module at the decoding end. The former embeds templates by a correlation-aware multiplicative spread spectrum with an adaptive amplitude, while the latter learns a calibrator to estimate the perspective projection. Experiments on both simulation and real-world scenarios support that the proposed scheme effectively resists camera-shooting attacks with various shooting conditions, regardless of whether the entire displayed images have been captured.

The risk of malicious exploitation of advanced image steganography necessitates the removal of hidden information from images. However, it is crucial to preserve the visual quality of the images undergoing processed. This paper suggests a geometrical attack in frequency domain (GAF) to address this challenge. GAF employs a thin plate spline (TPS) to slightly geometrically perturb the frequency components of the stego image. It incorporates a channel weight estimator and a frequency jammer. The channel weight estimator assigns perturbation strengths to each DCT channel, while the frequency jammer performs the TPS transform on the DCT channels using the assigned perturbation strengths. Experimental results demonstrate that the proposed approach effectively hinders secret image recovery with a little distortion to the stego images. Furthermore, it well preserves the visual quality of clear images that do not contain secret information.

Abstract In this paper, a geometrically invariant image watermarking scheme is proposed by exploiting multilevel histogram shapes. The embedding procedure starts by decomposing the host image with the first level Haar wavelet. After that, histograms are extracted from the approximation subband via several rounds, which are used to embed watermark bits. Each round of embedding first extracts a histogram at a specified level. Then the histogram is split into fragments, into which a number of watermark bits can be embedded. In this way, a considerable watermarking capacity is available. Besides, a histogram adjustment in the first embedding round is suggested to guarantee good population of histogram bins. Experimental results support its robustness against various common attacks and geometric attacks. Moreover, the scheme can embed multiple watermark sequences with various robustness and capacity profiles, which enriches its practical applications.

Due to the openness of wireless communication, information transmitted in the multiple access channel is vulnerable to eavesdropping by illegitimate users. Classic artificial noise-assisted schemes can improve security but are not suitable for power-constrained users. To address this problem, we propose a cross-time interference injection scheme in this paper, which improves security by introducing cross-time self-interference and inter-user interference, and does not require artificial noise. The proposed scheme exploits the properties of the Hadamard matrix and the wireless channels, so that the eavesdropping channel suffers from stronger interference than the legitimate channel. We analyze the security performance when both the legitimate receiver and the eavesdropper exploit the successive interference cancellation (SIC) scheme, including the secrecy sum rate, the secrecy capacity, and the secrecy outage probability. We further investigate the effect of the number of eavesdropper’s antennas, the number of legitimate users, and the number of time slots used by the scheme on the security performance, and obtain the corresponding relationship between these parameters when achieving a positive secrecy rate. Finally, simulation results corroborate our theoretical analysis.

Software vulnerabilities are one of the important reasons for network intrusion. It is vital to detect and fix vulnerabilities in a timely manner. Existing vulnerability detection methods usually rely on single code models, which may miss some vulnerabilities. This paper implements a vulnerability detection system by combining source code and assembly code models. First, code slices are extracted from the source code and assembly code. Second, these slices are aligned by the proposed code alignment algorithm. Third, aligned code slices are converted into vector and input into a hyper fusion-based deep learning model. Experiments are carried out to verify the system. The results show that the system presents a stable and convergent detection performance.

Vehicular ad-hoc networks (VANETs) have recently attracted considerable attention from both industry and academia for improving road safety and traffic efficiency. Trust modeling plays a significant role in VANETs, however, the existing trust models cannot primely conform to the characteristics of VANETs. This article proposes a novel trust cascading-based emergency message dissemination (TCEMD) model which incorporates the entity-oriented trust values into data-oriented trust evaluation in an efficient manner. In the proposed model, when an emergency event (e.g., an obstacle in front of the road) occurs, the emergency messages can be disseminated among the nearby vehicles in a trust cascading manner, where the entity-oriented trust values (which are evaluated and updated by leveraging the trust certificates and are contained in the messages) are adopted as important weights. Subsequently, the theoretical analysis for the robustness against several kinds of attacks and malicious behaviors, failure tolerance features, compatibility for several kinds of special situations, and incentive mechanisms in the TCEMD model are detailed. Afterwards, a series of simulations and analyses are conducted in a typical highway environment, and the results reveal that the proposed model significantly outperforms the existing models in several cases.

Some state-of-the-art binary image steganographic methods aim to generate stego images with good visual quality, while others focus more on the statistical security of the anti-steganalysis. This paper proposes a binary steganographic scheme that improves both of them by selecting more appropriate flipped pixels. First, a fused distortion measurement is developed that combines the advantages of flipping distortion measurement (FDM) and two data-carrying pixel location methods, including the edge adaptive grid method (EAG) and the “Connectivity Preserving” criterion (CPc). The FDM measures the distortion score by statistical features and achieves high-statistical security, while the EAG and CPc select pixels by analyzing the local texture structures based on visual quality. Then, to eliminate the interference brought by adjacent flipped pixels, a flipping position optimization strategy is proposed to find better positions for flipping pixels to further improve the steganographic performance. Experimental results have demonstrated that the proposed steganographic scheme can achieve stronger statistical security with better visual quality without degrading the embedding capacity.

Most state-of-the-art binary image steganographic techniques only consider the flipping distortion according to the human visual system, which will be not secure when they are attacked by steganalyzers. In this paper, a binary image steganographic scheme that aims to minimize the embedding distortion on the texture is presented. We extract the complement, rotation, and mirroring-invariant local texture patterns (crmiLTPs) from the binary image first. The weighted sum of crmiLTP changes when flipping one pixel is then employed to measure the flipping distortion corresponding to that pixel. By testing on both simple binary images and the constructed image data set, we show that the proposed measurement can well describe the distortions on both visual quality and statistics. Based on the proposed measurement, a practical steganographic scheme is developed. The steganographic scheme generates the cover vector by dividing the scrambled image into superpixels. Thereafter, the syndrome-trellis code is employed to minimize the designed embedding distortion. Experimental results have demonstrated that the proposed steganographic scheme can achieve statistical security without degrading the image quality or the embedding capacity.