The digital transformation of educational systems has significantly optimized administrative workflows and enhanced the user experience for educators and learners. However, the accumulation of sensitive personal data on educational websites has made them prime targets for cyber threats. Despite growing awareness of these security challenges, the technical roots of vulnerabilities within such platforms remain insufficiently explored. To address this gap, we introduce LLM-EduAttackGraph, a specialized tool designed to assist in vulnerability detection by leveraging large language models (LLMs). Rather than serving as a fully automated monitoring system, LLM-EduAttackGraph operates as a human-in-the-loop assistant, combining expert knowledge with the analytical capabilities of LLMs to help identify potential penetration paths based on network fingerprint information. Using LLM-EduAttackGraph, we have so far identified 961 penetration vulnerabilities across educational websites in mainland China—a number that continues to grow as analysis progresses. These findings demonstrate the tool’s practical value in augmenting cybersecurity research and efforts. Our in-depth analysis of the discovered vulnerabilities reveals that limited developer experience and a heavy dependence on outsourced website development are key contributing factors. By shedding light on these root causes, our research offers actionable strategies and insights aimed at improving the cybersecurity posture of educational platforms and ensuring the sustainable development of online education. Furthermore, we have compared LLM-EduAttackGraph with several existing large model penetration tools to demonstrate the performance of LLM-EduAttackGraph. Such strengths include its low demand for hardware resources and having undergone empirical verification.

Asymmetric numeral systems (ANS) is a widely used entropy coding method in commercial compressors due to its high performance. Joint compression and encryption techniques can offer reliability and cost-effectiveness for secure Big Data storage. However, existing joint compression-encryption schemes for ANS coding often suffer from either increased storage space requirements or limited security. To address these issues, this paper proposes two ANS-based joint compression-encryption algorithms that provide considerable security with almost no compression loss. The first scheme, based on interval swapping, employs a cryptographically secure ChaCha20 generator to perturb the order of contiguous intervals, thereby introducing controlled randomness into the encoding process. The second scheme, based on interval splitting, discards the conventional assumption of representing each symbol with a single contiguous interval, instead assigning multiple sub-intervals to enhance both security and flexibility. In addition, a sequence of output permutations is applied to further strengthen resistance against attacks. Experimental results show that the proposed methods reduce compression loss by approximately 3.83% compared with existing schemes, while the interval swapping scheme achieves a 46.9% reduction in time cost. Security analysis confirms that the enlarged key space significantly increases robustness against brute-force attacks. These results demonstrate that the proposed approaches effectively balance compression efficiency and encryption strength, offering a lightweight and secure solution for Big Data storage.

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.

With the proliferation of AI agents in various domains, protecting the ownership of AI models has become crucial due to the significant investment in their development. Unauthorized use and illegal distribution of these models pose serious threats to intellectual property, necessitating effective copyright protection measures. Model watermarking has emerged as a key technique to address this issue, embedding ownership information within models to assert rightful ownership during copyright disputes. This paper presents several contributions to model watermarking: a self-authenticating black-box watermarking protocol using hash techniques, a study on evidence forgery attacks using adversarial perturbations, a proposed defense involving a purification step to counter adversarial attacks, and a purification-agnostic curriculum proxy learning method to enhance watermark robustness and model performance. Experimental results demonstrate the effectiveness of these approaches in improving the security, reliability, and performance of watermarked models.

Adversarial training often suffers from a robustness-accuracy trade-off, where achieving high robustness comes at the cost of accuracy. One approach to mitigate this trade-off is leveraging invariance regularization, which encourages model invariance under adversarial perturbations; however, it still leads to accuracy loss. In this work, we closely analyze the challenges of using invariance regularization in adversarial training and understand how to address them. Our analysis identifies two key issues: (1) a "gradient conflict" between invariance and classification objectives, leading to suboptimal convergence, and (2) the mixture distribution problem arising from diverged distributions between clean and adversarial inputs. To address these issues, we propose Asymmetric Representation-regularized Adversarial Training (ARAT), which incorporates asymmetric invariance loss with stop-gradient operation and a predictor to avoid gradient conflict, and a split-BatchNorm (BN) structure to resolve the mixture distribution problem. Our detailed analysis demonstrates that each component effectively addresses the identified issues, offering novel insights into adversarial defense. ARAT shows superiority over existing methods across various settings. Finally, we discuss the implications of our findings to knowledge distillation-based defenses, providing a new perspective on their relative successes.

Content creation has dramatically progressed with the rapid advancement of large language models like ChatGPT and Claude. While this progress has greatly enhanced various aspects of life and work, it has also negatively affected certain areas of society. A recent survey revealed that nearly 30% of college students use generative AI to help write academic papers and reports. Most countermeasures treat the detection of AI-generated text as a binary classification task and thus lack robustness. This approach overlooks human involvement in the generation of content even though human-machine collaboration is becoming mainstream. Besides generating entire texts, people may use machines to complete or revise texts. Such human involvement varies case by case, which makes binary classification a less than satisfactory approach. We refer to this situation as participation detection obfuscation. We propose using BERTScore as a metric to measure human involvement in the generation process and a multi-task RoBERTa-based regressor trained on a token classification task to address this problem. To evaluate the effectiveness of this approach, we simulated academic-based scenarios and created a continuous dataset reflecting various levels of human involvement. All of the existing detectors we examined failed to detect the level of human involvement on this dataset. Our method, however, succeeded (F1 score of 0.9423 and a regressor mean squared error of 0.004). Moreover, it demonstrated some generalizability across generative models. Our code is available at https://github.com/gyc-nii/CAS-CS-and-dual-head-detector

Face manipulation technology poses increasingly significant threats to privacy and credibility in digital media. Existing countermeasures, particularly watermark-based methods, often fail to provide comprehensive protection for multiple tasks due to reliance on a single property. In response, we present VIP (Versatile Identity Protection), an integrated framework for defending against identity-swapping attacks that seamlessly unifies manipulation detection, tampering localization, database-free authentication, and identity restoration. VIP embeds user-specific ID messages via a dual-property watermarking system that harnesses semi-fragility to precisely localize tampered regions and leverages robustness to preserve identity information for authentication and restoration. To effectively embed high-dimensional real-valued ID messages throughout entire images, we design an ID-aware Progressive Watermark Encoder (ID-PWE). Through extensive experiments, we demonstrate that VIP significantly outperforms existing solutions under various unseen identity-swapping attacks.

Reversible data hiding in encrypted images (RDHEI) is a technique that not only allows the cover images can be fully restored without any loss of information after the embedded data has been extracted but also ensures the confidentiality within the cover images. This article proposes an RDHEI scheme combining adaptive ( n , n ) secret image sharing (SIS) manner. The content owner reserves part of the least significant bit plane (LSBP) in cover images by two most significant bit planes (MSBPs) compression using the median edge detector (MED) prediction method. To level up the privacy protection of n cover images, a two‐layer encryption method is utilized to generate n shares, that is, the self‐encryption and cross‐encryption. Moreover, our method can be applied on no matter how many of cover images. The secret data with identification can be concealed by the data hiders into the vacated LSB of their own shares. Through the cooperation of the overall shares, the receiver can retrieve the embedded secret data and recover the cover images. Experiment results reveal the security reliability of our approach and the outstanding performance when compared to some related methods. Also, the approach can be employed in color image domain.

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEIs) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding (RDH) with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the Chinese remainder theorem for polynomials (CRTPs). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach usingxorpreservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, number of pixels change rate and unified average changing intensity. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bits per pixel (bpp) and 2.0656 bpp, respectively.

Since the COVID-19 outbreak, there has been a growing need for contactless healthcare to meet medical diagnosis demands. Electronic health systems using the Internet of Things (IoT) are rapidly advancing, transmitting significant amounts of private medical data online. In telemedicine, where patients are diagnosed remotely, sensitive information, such as patient records, may be embedded into medical images for security purposes. Due to the large file sizes of medical images produced by equipment, compression is essential for fast transmission. To safeguard medical images in telemedicine and address bandwidth limitations, we utilize data hiding techniques and absolute moment block truncation coding (AMBTC) compression to introduce an IoT-driven electronic health protection mechanism. Our mechanism employs diverse methods to compress and embed data across various image blocks. Additionally, it offers a flexible adaptation to meet different application requirements concerning embedding capacity, visual quality, and file size by adjusting thresholds and variants. Compared to alternative methods, our approach delivers superior payload capacity and efficiency while preserving visual fidelity.

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.

Pixel value ordering (PVO) is an efficient method for implementing reversible data hiding, which can achieve embedding based on overlapping pixel blocks when combined with the flexible patch moving (FPM) mode, especially the two-dimensional (2D) FPM mode. However, the existing 2D FPM mode, whose pairing way of prediction error is not conducive to generating more pixel blocks available for embedding, and whose movement rules are too inefficient to fully exploit the potential of the PVO, results in wasting many available blocks. Therefore, in this paper, a two-stage embedding mechanism is proposed for the 2D FPM mode, in which the combination of prediction errors is adjusted to improve the possibility of generating available blocks and the two-stage embedding doubles the number of pixel blocks available for embedding. Furthermore, an FPM mode selection is proposed, where four novel 2D FPM modes are designed to efficiently exploit the potential of the PVO according to the different directional gradients. Lastly, a set of efficient 2D mappings is well-designed for multiple histograms to achieve lower embedding distortion. The extensive experimental results show that the proposed method outperforms other state-of-the-art methods in terms of embedding capacity and image fidelity. The average peak signal-to-noise ratio for the Kodak image dataset is as high as 63.62 dB after embedding 10,000 bits.

Deep neural networks are highly vulnerable to adversarial examples, which are inputs with small, carefully crafted perturbations that cause misclassification—making adversarial attacks a critical tool for evaluating robustness. Existing black-box methods typically entail a trade-off between precision and flexibility: pixel-sparse attacks (e.g., single- or few-pixel attacks) provide fine-grained control but lack adaptability, whereas patch- or frequency-based attacks improve efficiency or transferability, but at the cost of producing larger and less precise perturbations. We presentGreedyPixel, a fine-grained black-box attack method that performsbrute-force-style, per-pixel greedy optimizationguided by a surrogate-derived priority map and refined by means of query feedback. It evaluates each coordinate directlywithout any gradient information, guaranteeing monotonic loss reduction and convergence to a coordinate-wise optimum, while also yielding near white-box-level precision and pixel-wise sparsity and perceptual quality. On the CIFAR-10 and ImageNet datasets, spanning convolutional neural networks (CNNs) and Transformer models, GreedyPixel achieved state-of-the-art success rates with visually imperceptible perturbations, effectively bridging the gap between black-box practicality and white-box performance. The implementation is available at https://github.com/azrealwang/greedypixel.

This article proposes a robust decoupling network named RDNet to provide stable traffic predictions even when perturbations exist in historical data. A decoupling block is designed in the RDNet for dividing traffic data into the invariable component (IC) and variable component (VC). The IC of historical or future data is estimated through the invariable block without historical data and thus would not be perturbed. The variable block is developed to forecast the VC of future data using the VC of historical data. Besides, the robust graph neural network and smoothing loss are designed to reduce the effects of perturbations. The RDNet fuses the obtained IC and VC of future data to produce the predictions, and the invariable and decoupling losses are developed for stabilizing the prediction. The results on six open datasets have demonstrated that the RDNet can achieve a 15.62% average improvement in accuracy compared with the state-of-the-art predictor.

The graph-based traffic forecasting is generally realized on the assumption of sufficient data, which could be impractical in the regions without well-deployed mobile sensors or data-processing facilities. Recent studies have developed a solution with the cross-region transfer learning, i.e. transferring traffic knowledge from the source regions to target ones, whose traffic data and computing resources are limited. Nevertheless, relevant issues, including initialization selection and domain adaptation, have not been effectively tackled in the cross-region graph-based traffic forecasting. This paper proposes the cluster-granularity spatiotemporal transfer (CGSTT), which transfers the cluster-granularity knowledge from the source region to target one for the cross-region graph-based traffic forecasting, as not all source knowledge is positive to the target region. Additionally, the domain adaptation is achieved by the dual alignment consisting of the covariate alignment and label alignment of the source/target data, making the proposed CGSTT adapt to the target region efficiently. The superiority of the proposed method over ten compared baseline methods for both short-term and long-term predictions is demonstrated by the conducted experiments on four tasks, which show that it outperforms the state-of-the-art method by achieving an 8.89% average improvement in forecasting accuracy. The PyTorch implementation of the CGSTT is available athttps://github.com/canyangguo/CGSTT.

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.

To achieve accurate traffic forecasting, previous research has employed inner and outer aggregation for information aggregation, and attention mechanisms for heterogeneous spatiotemporal dependency learning, which results in inefficient model learning. While learning efficiency is critical due to the need for updating frequently the model to alleviate the impact of concept drift, limited work has focused on improving it. For efficient learning and accurate forecasting, this study proposes the dynamic spatiotemporal straight-flow network (DSTSFN). Breaking the aggregation paradigms employing both inner and outer aggregation, which may be redundant, the DSTSFN designs a straight-flow network that employs bipartite graphs to learn directly the dependencies between the source and target nodes for outer aggregation only. Instead of the attention mechanisms, the dynamic graphs/networks, which outdo static ones by possessing time-varying dependencies, are designed in the DSTSFN to distinguish the dependency heterogeneity, making the model relatively streamlined. Additionally, two learning strategies based on respectively the curriculum and transfer learning are developed to further improve the learning efficiency of the DSTSFN. Our study could be the first work designing the learning strategies for the multi-step traffic predictor based on dynamic spatiotemporal graphs. The learning efficiency and forecasting accuracy are demonstrated by experiments, which show that the DSTSFN can outperform not only the state-of-the-art (SOTA) predictor for accuracy by achieving a 2.27% improvement in accuracy and requiring only 8.98% of the average training time, but also the SOTA predictor for efficiency by achieving a 9.26% improvement in accuracy and requiring 91.68% of the average training time.

Model watermarking Model watermarking is a method for embedding watermark information into a neural network model. It proves the ownership of the model without affecting its performance. Since there are plenty of attacks against model pruning, it becomes more significant to design anti-pruning model watermarking algorithms. In this paper, multiple watermark embedding is performed to protect the model copyright for the image steganography auto-encoder model “Hiding Data with Deep Networks” (HiDDeN). Firstly, the appropriate model weights are selected by employing three classical model pruning algorithms of model weights. Secondly, the model watermark is spread by using Discrete Cosine Transform (DCT)-based image watermarking algorithm, which improves the noise and pruning resistance of the model watermark. Finally, the model watermark is embedded to the 4th and 5th decimal places of the selected model weights. The experimental results demonstrate that the proposed algorithm has a good robustness against model pruning without affecting the watermark extraction performance of the auto-encoder network model. Even with the embedded model watermark, the decoder's watermark extraction accuracy is still higher than 0.9993. and the autoencoder is still valuable when the model weights are pruned by 40%. Furthermore, the proposed algorithm has a certain degree of improvements in watermarking capacity.

Text authentication serves a vital role in the defense of digital identity and content against various types of cybercrime. The use of a digital signature is a common cryptographic technique for text authentication. Linguistic steganography can be applied to further conceal a digital signature within the corresponding text to facilitate data management. However, steganographic distortion lurking in the text, albeit almost imperceptible, has the potential to cause automatic computing machinery to make biased decisions. This has led to an interest in the pursuit of reversibility, the ability to reverse a steganographic process and remove distortion. In this article, we propose a reversible steganographic system for natural language text. We use a pre-trained transformer neural network for masked language modeling and embed messages in a reversible manner via predictive word substitution. Furthermore, we derive an adaptive steganographic route by taking account of predictive uncertainty, which is quantified based on a theoretical framework of Bayesian deep learning. Experimental results show that the proposed steganographic system can attain a proper balance between capacity, imperceptibility, and reversibility with close semantic and sentimental similarities between cover and stego texts.

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 graph convolution network (GCN), whose flexible convolution kernels perfectly adapt to the complex topology of the road network, has gradually dominated the spatiotemporal dependency learning of traffic flow data. Defining and learning the spatiotemporal characteristics and relationships of the traffic network efficiently and accurately, which are the important prerequisites for the success of the GCN, have become one of the most burning research problems in the field of intelligent transportation systems. This paper proposes a fast spatiotemporal learning (FSTL) framework containing the fast spatiotemporal GCN module, which reduces the computational complexity of the spatiotemporal GCN from${\cal O(k^{2})}$to${\mathcal{ O(k)}}$, where$k$is the number of time steps of data learned in each GCN operation. To mine globally and fast the correlations of road node pairs, a correlation analysis based on the normal distribution with the complexity of${\mathcal{ O(N)}}$, where$N$is the number of nodes in the traffic network, is proposed to construct the global correlation matrix. Besides, the multi-scale temporal learning is integrated into the FSTL to overcome the receptive field constraints of the spatiotemporal GCN. The experimental results on four real-world datasets demonstrate that the FSTL achieves 48.88% and 5.26% reductions in the training time and mean absolute error, respectively, compared with the state-of-the-art model.

Authentication mechanisms are at the forefront of defending the world from various types of cybercrime. Steganography can serve as an authentication solution through the use of a digital signature embedded in a carrier object to ensure the integrity of the object and simultaneously lighten the burden of metadata management. Nevertheless, despite being generally imperceptible to human sensory systems, any degree of steganographic distortion might be inadmissible in fidelity-sensitive situations such as forensic science, legal proceedings, medical diagnosis and military reconnaissance. This has led to the development of reversible steganography. A fundamental element of reversible steganography is predictive analytics, for which powerful neural network models have been effectively deployed. Another core element is reversible steganographic coding. Contemporary coding is based primarily on heuristics, which offers a shortcut towards sufficient, but not necessarily optimal, capacity–distortion performance. While attempts have been made to realise automatic coding with neural networks, perfect reversibility is unattainable via such learning machinery. Instead of relying on heuristics and machine learning, we aim to derive optimal coding by means of mathematical optimisation. In this study, we formulate reversible steganographic coding as a nonlinear discrete optimisation problem with a logarithmic capacity constraint and a quadratic distortion objective. Linearisation techniques are developed to enable iterative mixed-integer linear programming. Experimental results validate the near-optimality of the proposed optimisation algorithm when benchmarked against a brute-force method.

In this paper, we propose an effective image retrieval approach using block truncation coding compressed data stream based on edge-based quantization (EQBTC). First, an image is compressed into corresponding quantisers and a bitmap image by EQBTC. Then, the quantisers are used for colour feature extraction, whereby the bitmap image and grey image are used for luminance and edge feature extraction. Subsequently, two image features, the colour histogram feature (CHF) and the overall structure feature (OSF), are computed to measure the similarity between two images using a specific distance metric computation. The results presented in this paper demonstrate that the proposed model is superior to the block truncation coding image retrieval scheme and some earlier proposed methods.

In this paper, we show how a dealer with limited resources is possible to share the secrets to players via an untrusted cloud server without compromising the privacy of the secrets. This scheme permits a batch of two secret messages to be shared to two players in such a way that the secrets are reconstructable if and only if two of them collaborate. An individual share reveals absolutely no information about the secrets to the player. The secret messages are obfuscated by encryption and thus give no information to the cloud server. Furthermore, the scheme is compatible with the Paillier cryptosystem and other cryptosystems of the same type. In light of the recent developments in privacy-preserving watermarking technology, we further model the proposed scheme as a variant of reversible watermarking in the encrypted domain.

In this study, the authors propose a novel (2, 2) secret image sharing scheme in which a control parameter ω is used to change the payload easily. Since the modification of the original cover pixel values can be limited within a small range according to the value of ω , the shadow images can achieve excellent visual quality. In the extracting process, the secret image and the cover image can be reconstructed correctly. Experimental results showed that the authors’ proposed scheme can enhance the embedding rate significantly, up to 3 bpp if ω is set to 6. In addition, the peak signal‐to‐noise ratio values of the shadow images are still satisfactory when the embedding rate approaches a very high value. Comparisons demonstrated that their proposed scheme outperforms other schemes that have been developed recently in terms of the embedding rate and the visual quality.

Data hiding is a technique for sending secret information under the cover of the digital media. It is usually used to protect privacy and sensitive information when such information is transmitted via a public network. To date, high capacity remains one of the most important research aspects of data hiding. In this study, a new, turtle shell‐based data hiding scheme is proposed to improve embedding capacity further while guaranteeing good image quality. In the proposed, turtle shell‐based scheme, a reference matrix is composed and a location table is generated. Then, according to the reference matrix and the location table, each pixel pair is processed to conceal four secret bits. The experimental results indicated that the proposed scheme achieved higher embedding capacity and lower distortion of images than some existing schemes.