As industrial systems increasingly rely on 3D mesh models to bridge the physical and digital domains, ensuring their secure and efficient management has become critical. While thumbnail-preserving encryption (TPE) has successfully balanced security and usability for 2D images, extending this concept to 3D models remains largely unexplored. In this work, a novel distributed trust and visually meaningful secret sharing scheme for 3D mesh models (3D-VMSS) is proposed. The distributed trust mechanism splits the model data among multiple participants, ensuring that no single party possesses sufficient information for reconstruction. The original model can only be reconstructed through collaboration among a predefined threshold of authenticated participants. This approach fundamentally differs from traditional single-key encryption by eliminating single points of failure and enabling flexible access control. The scheme segments vertex coordinates into hierarchical components and applies polynomial secret sharing to ensure confidentiality, generating visually meaningful shares that preserve recognizable geometric features while concealing sensitive details. To ensure integrity and resist collusion attacks, dual authentication mechanisms are incorporated. Furthermore, progressive reconstruction enables different quality levels based on participant collaboration. Experimental results demonstrate the scheme's effectiveness in balancing security and practical usability for distributed 3D model management.

Reversible data hiding (RDH) provides a practical solution for the secure storage and transmission of sensitive data in cloud environments. With the increasing adoption of three-dimensional (3D) mesh models in fields that require high privacy and intellectual property protection, RDH techniques specifically designed for these models have gained considerable attention. However, existing RDH schemes for 3D mesh models often encounter limitations such as a low embedding capacity, low runtime efficiency, or insufficient security. To address these challenges, this paper proposes a novel privacy preservation scheme that integrates the geometric projection strategy with Elliptic Curve Integrated Encryption (ECIE). The geometric projection strategy effectively exploits local geometric regularities within mesh models, thereby enhancing the vertex prediction accuracy. The integration of ECIE into the RDH framework further strengthens security by mitigating the risks associated with symmetric key transmission, providing enhanced protection tailored to customized data. Experimental results demonstrate that compared to state-of-the-art methods, the proposed scheme achieves superior embedding capacity and vertex utilization rate while maintaining perfect reversibility, separable data extraction, and high runtime efficiency.

Crypto-space reversible data hiding (RDH) has emerged as an effective technique for transmitting secret information over the Internet. However, most existing schemes are designed for uncompressed images, while almost all images are processed and transmitted in compressed formats. There is an urgent need to develop methods for compressed images, such as joint photographic experts group (JPEG). In this article, we propose an RDH in encrypted JPEG images, where the bitstreams of alternating current (AC) coefficients and the secret data are mapped to numbers over Galois field. The obtained numbers are then utilized to conduct a polynomial for secret sharing. By reproduction into secret shares, the AC coefficients and the secret data are secured. In addition, a block sorting strategy is used to reduce image distortion under low data payload. Experimental results demonstrate that the proposed scheme outperforms state-of-the-art methods in embedding capacity while preserving the file size and conforming to the JPEG format.

With the exponential growth of network computing demands, service function chain (SFC) scaling can meet the evolving demands and provide more service functionalities, which is crucial for addressing the shortcomings of network resources. However, in multidomain and heterogeneous edge networks, existing SFC scaling methods aim to reduce the additional costs of delays and resources during scaling, which ignore the resource redundancy and accumulation caused by the high and low pressure of virtual network load. Additionally, the inappropriateness of selection order of interdomain transit nodes and intradomain service nodes leads to frequent SFC scaling and placement. To tackle these challenges, we propose a relative-cost-aware SFC collaborative scaling and placement (SFC-CSP) mechanism based on multiagent reinforcement learning (MARL) with Stackelberg game. First, we introduce a priority-based virtual network functions scaling queue to reduce the times of frequent SFC scaling. Then, to alleviate the imbalance between delay, resource redundancy, and resource accumulation, we establish a relative cost-based multiobjective optimization function. The aim is to minimize the delay cost, the relative computing resource cost, the storage resource cost, and bandwidth resource cost. Furthermore, to reduce the impact of selection order for interdomain transit node and intradomain service node on asynchronous SFC placement, we design an SFC-CSP mechanism based on MARL with Stackelberg game, considering the interaction between node action selections. Experimental results demonstrate that our proposed method not only achieves higher SFC acceptance rates compared with other methods but also performs well in reducing end-to-end delay of SFC and resource accumulation.

Abstract Facial recognition systems have emerged as indispensable components in identity verification. These systems heavily rely on facial data, which is stored in a biometric database. However, storing such data in a database raises concerns about privacy breaches. To address this issue, several technologies have been proposed for protecting facial biometrics. Unfortunately, many of these methods can cause irreversible damage to the data, rendering it unusable for other purposes. In this paper, we propose a novel reversible anonymization scheme for face images via cyclic learning. In our scheme, face images can be de-identified for privacy protection and reidentified when necessary. To achieve this, we employ generative adversarial networks with a cycle consistency loss function to learn the bidirectional transformation between the de-identified and re-identified domains. Experimental results demonstrate that our scheme performs well in terms of both de-identification and reidentification. Furthermore, a security analysis validates the effectiveness of our system in mitigating potential attacks.

Image covert communication camouflages or conceals sensitive information in regular digital images during transmission to avoid detection or interception. It is widely used in military, intelligence, and law enforcement agencies. Data hiding is a feasible solution for covert communication, which embeds confidential information in cover media while preserving its visual appearance. In this letter, we propose an image reversible data hiding (RDH) scheme based on block regulation, which embeds secret data using pixel permutation. Traditional permutation-based data embedding methods usually suffer from error restoration of the cover image. To solve this problem, we propose a regulation operation to pre-process the image blocks, and thus most of the image blocks are converted into regular blocks. The regulated image blocks can be exploited to embed secret data without restoration error. In addition, we propose an adaptive strategy to generate permutation table dynamically, which further enhances the security level. Experimental results indicate that the proposed scheme provides a good embedding capacity and preserves a high level of image visual quality.

Reversible data hiding in encrypted images (RDHEI) can serve as a technical solution to secure data in applications that rely on cloud storage. The key features of an RDHEI scheme are reversibility, security, and data embedding rate. To enlarge the embedding rate, this paper proposes a novel RDHEI scheme based on the median edge detector (MED) and a new proposed hierarchical block variable length coding (HBVLC) technique. In our scheme, the image owner first predicts the pixel values of the carrier image with MED. Then, the prediction error array is sliced into bit-planes and encoded plane by plane. By leveraging the inherent features of the prediction error bit-planes, the image owner adaptively decomposes a bit-plane into blocks of different hierarchical levels based on its local smoothness and encodes the blocks with a variable length coding method. As a result, the carrier image is efficiently compressed to provide spare room for data embedding. The encoded carrier image is then processed with the conventional steps of an RDHEI technique. Experimental results show that the proposed scheme not only can restore the secret data and the carrier image without loss but also outperforms state-of-the-art methods in the embedding rate for images with various features.

The combination of convolution and Transformer applied to medical image segmentation has achieved great success. However, it still cannot reach extremely accurate segmentation on complex and low-contrast anatomical structures under lower calculation. To solve this problem, we propose ECT-NAS method to automatically search Efficient CNN-Transformers architecture for medical image segmentation, which featured with multi-scale search space. To better extract the global context in the search space of ECT-NAS, we carefully design a light transformer with local-global attention. Last, we proposed an efficient resource constrained search strategy that simultaneously optimizes the accuracy and cost (Params/FLOP) of the model. We evaluate ECT-NAS by conducting extensive experiments on synapse multi-organ, Chaos and ACDC datasets, showing that this approach achieves competitive performance over other segmentation methods, with fewer parameters and lower FLOPs.

In the information age, a digital image is an important media for people’s daily interactions. Looking to maintain the quality of the restored image, how to maximise the compression of images has become a challenging topic. Vector quantisation (VQ) compression is an easy-operating image compression method that can compress images to 1/16th of the original size. Based on VQ compresses, a novel image compression method is proposed in this paper. The proposed scheme compresses the image depending on the result of linear regression prediction which can significantly increase the compression ratio.