Unauthorized screen capturing and dissemination pose severe security threats such as data leakage and information theft. Several studies propose robust watermarking methods to track the copyright of Screen-Camera (SC) images, facilitating post-hoc certification against infringement. These techniques typically employ heuristic mathematical modeling or supervised neural network fitting as the noise layer, to enhance watermarking robustness against SC. However, both strategies cannot fundamentally achieve an effective approximation of SC noise. Mathematical simulation suffers from biased approximations due to the incomplete decomposition of the noise and the absence of interdependence among the noise components. Supervised networks require paired data to train the noise-fitting model, and it is difficult for the model to learn all the features of the noise. To address the above issues, we propose Simulation-to-Real (S2R). Specifically, an unsupervised noise layer employs unpaired data to learn the discrepancy between the modeled simulated noise distribution and the real-world SC noise distribution, rather than directly learning the mapping from sharp images to real-world images. Learning this transformation from simulation to reality is inherently simpler, as it primarily involves bridging the gap in noise distributions, instead of the complex task of reconstructing fine-grained image details. Extensive experimental results validate the efficacy of the proposed method, demonstrating superior watermark robustness and generalization compared to state-of-the-art methods.

With the rapid development of e-health technology and artificial intelligence, healthcare services have undergone significant digitalization and intelligence transformation. This drives the emergence of innovative models such as personalized care and telemedicine, making electronic medical information sharing a critical research focus. However, due to the high sensitivity of medical data, existing sharing platforms face challenges, including insufficient privacy protection, inadequate guarantees of data integrity and authenticity, and reliance on centralized systems. These issues can lead to risks including identity information leakage, data tampering, and single points of failure. To address these challenges, this paper proposes a blockchain-based framework for electronic medical information sharing, namely PCTRS, which stands for Privacy and Control in Electronic Medical Records through Blockchain and Threshold Traceable Ring Signatures. The framework integrates InterPlanetary File System for off-chain storage, reducing costs while enhancing data security through Elliptic Curve Diffie–Hellman Ephemeral key exchange and Advanced Encryption Standard – Galois/Counter Mode encryption. By integrating smart contracts with a threshold-based traceable ring signature scheme, the system achieves an optimal balance between privacy protection and transparency. Furthermore, the system implements a reputation mechanism combined with decentralized arbitration to ensure reliability, effectively mitigating security risks in medical data sharing.

The quantization step is a crucial parameter in the JPEG compression process, and provides prior knowledge for JPEG image steganography and forensics. Existing neural network-based methods typically estimate the quantization steps for all discrete cosine transform (DCT) subbands jointly, by treating the entire quantization table as a unified input and leveraging the inter-subband relationships. However, subband relationships vary across different quantization tables, leading to poor generalization for methods that rely heavily on such relationships. To address the above issues, we depart from the strategy that relies on inter-subband relationships and instead train the model on a specific single subband. To compensate for the possible decrease in accuracy due to the lack of relationships between subbands, we extract the ranking features and histogram features from the DCT coefficient histograms of the subbands. Ranking features capture local patterns in DCT histograms by modeling the relative relationships between neighboring coefficients, thereby compensating for the absence of local detail. On the other hand, histogram features represent the overall distribution pattern of the DCT coefficient histograms and capture the global trends and statistical properties in the subbands. We subsequently employ convolutional groups and multilayer perceptron (MLP) structures to extract compression artifacts from these two features. Finally, we introduce a comprehensive evaluation metric, called GenAQt, to quantify the algorithm’s generalization ability across quantization tables. The experimental results demonstrate that our method maintains high accuracy across quantization tables, with RelGenAQt (relative accuracy decrease) exceeding 81% and AbsGenAQt (absolute accuracy decrease) being less than 0.38.

Passive sensing based on UHF RFID has increasingly proven its efficacy in various applications. However, existing security methods often face challenges in implementation on passive tags or fail to meet the high data read rates required by array sensing operations. To address these constraints, we introduce a novel security framework called PSC-tags, which is in line with the zero-trust security concept and well-suited for parallel deployment within RFID sensing array scenarios. PSC-tags leverages the phase sequence similarities inherent in tag arrays, leading to the development of an optimal tag group selection strategy. Concurrently, we customize a convolutional neural network incorporating an attention mechanism for authentication. This method is applied to XRF55, which is a comprehensive dataset of human indoor activities, as well as a sub–dataset collected in real–world scenarios. Extensive experimental results demonstrate the effectiveness of PSC-tags, with an average accuracy of 98.5% and only 56 milliseconds of authentication time per sample required. Notably, PSC-tags is compatible with commercial off-the-shelf (COTS) devices and does not require any additional data acquisition. The method significantly fortifies the defenses against multiple attacks within RFID array sensing.

Visible watermarks are generally embedded into digital images to claim their ownership for copyright protection. Unfortunately, the watermark removal models based on Deep Neural Networks (DNNs) are able to remove the watermarks from watermarked images, posing a great threat to image copyright protection. To prevent the watermark from being removed, watermark vaccines, i.e., adversarial perturbations, are usually added to the watermarked images to attack the target models, making them unable to remove the watermarks. However, the existing approaches indiscriminately add the watermark vaccine to the whole image region, and have not considered the vaccine failure caused by image noises, thereby still suffering from the issues of low imperceptibility and robustness. To address the above issues, we propose an Attention-Guided Watermark Vaccine (AGWV) scheme. Specifically, we propose pixel-feature attention (PFA) to identify the proper region for adding watermark vaccine, so as to achieve high imperceptibility for the added watermark vaccine. Then, we adopt image noises to perturb the vaccinated images and further optimize the watermark vaccine to correct the attention bias caused by image noise, thereby enhancing the robustness of watermark vaccines. Moreover, we design a vaccine evaluation model to intuitively evaluate the protective performances of watermark vaccines. Extensive experiments demonstrate that the proposed AGWV outperforms the state-of-the-arts in the aspects of both imperceptibility and robustness for defending against watermark removal models. Supplementary Material is available at https://github.com/YujiangLi0v0/ICME25.git

Deep neural networks (DNNs) based watermarking algorithms have made significant strides in recent years. However, existing methods either demand substantial resources for image feature extraction during watermark embedding, sacrificing efficiency, or completely neglect image texture information, resulting in suboptimal performance. Moreover, current algorithms struggle with real-time watermark extraction. To address these limitations, we propose a lightweight conditional residual watermarking (CReW) architecture. Specifically, CReW employs a Conditional Generative Adversarial Network (CGAN) framework to generate an adaptive residual image guided by the structure of the cover image, which is decoupled from the network to reduce computational complexity. This design enables CReW to achieve an optimal balance between performance and efficiency. Additionally, by directly optimizing the residual image to capture variations in watermark behavior under distortion, CReW significantly enhances robustness. Furthermore, we design redundancy coding blocks to increase the mutual information of the watermark, along with a patch-level discriminator to improve local patch discrimination, thereby further enhancing image quality. Finally, by reducing channel redundancy and leveraging FasterNet, we developed a low-complexity network architecture, FasterCReW, which facilitates real-time watermark embedding and extraction. Extensive experimental results demonstrate that, despite having$36 \times $fewer network parameters and$30\times $fewer floating point operations (FLOPs) than Adaptor, FasterCReW exhibits excellent robustness against distortions such as cropout, JPEG compression, and Gaussian noise. Furthermore, FasterCReW significantly outperforms other existing DNN-based watermarking algorithms in terms of running speed, achieving an$8\times $speed increase over UDH and a$28\times $increase over Adaptor on an Intel Core i7-8750H CPU.

In recent years, blockchain-based data trading platforms have gained great popularity in many fields because blockchain is decentralized, transparent, antitamper, and traceable. However, data trading for medical images still has problems such as privacy protection and copyright protection, so we propose a blockchain-based data trading scheme for medical images with copyright and privacy protection. Our scheme utilizes smart contracts in the blockchain to achieve automation and intelligence of data trading. We also combine digital watermarking and image encryption to further solve copyright, secure transmission, and privacy issues during medical image transactions. We conduct relevant experiments and verify the feasibility of our proposed scheme.

Data trading has become a pivotal element in modern business models, facilitating data exchange, however, it may give rise to potential disputes. Existing data trading models resort to centralized arbitration to resolve disputes, potentially compromising the transparency and reliability of the trading. Recent research has proposed the use of decentralized arbitration to overcome these challenges. However, practical and viable decentralized arbitration models are currently lacking. Existing decentralized arbitration solutions for data trading primarily focus on tackling specific arbitration issues without providing a comprehensive mechanism. Therefore, we have developed a comprehensive data trading model, BlockArb, featuring a decentralized arbitration mechanism. We present the details of BlockArb, addressing the challenges of the decentralized arbitration system. We conducted a performance analysis of this model, revealing the feasibility of this approach in numerous scenarios. We highlight the superiority of our proposed scheme by comparing it with existing arbitration methods employed in data trading. Our experimental results unequivocally validate the effectiveness and reliability of our model.

In the past few years, there has been an increasing focus on deep learning-based watermarking techniques. Many existing methods do not impose constraints to guide the embedding of watermarking, which leads to random embedding positions and makes watermarks vulnerable to detection and attack. In this paper, an adaptive robust watermarking technique is proposed as a solution to this issue. The proposed method employs a new embedding-guided end-to-end architecture, introducing the Embedding Guide component that utilizes attention mechanism and edge information to embed the secret message into regions that are visually insensitive and inconspicuous. This component enables adaptive embedding of the secret message in each cover image, resulting in high-quality watermarked images with improved imperceptibility. To enhance robustness, this study integrates the Efficient Channel Attention (ECA) block into both the message preprocessor and decoder, facilitating more effective secret message embedding and extraction. Furthermore, UNet++ is applied to improve performance against combined noise. The experimental findings demonstrate that the suggested algorithm surpasses current approaches.

Abstract Voting plays a vital role in democratic societies. Adopting electronic voting can effectively increase voter participation and significantly reduce the financial burden on the organizers. In recent years, with the prevalence of blockchain technology, numerous blockchain-based electronic voting schemes have emerged. Compared with traditional electronic voting schemes, they have more favorable security features. However, existing schemes generally suffer from inefficient voting procedures, limited functionality, and dependence on specific blockchain platforms, making them challenging to deploy in diverse voting scenarios. This paper proposes an efficient and versatile electronic voting scheme on blockchain that addresses these problems using our proposed smart contract-based aggregated blind signature, zero-knowledge proofs, and threshold encryption scheme. In the paper, the scheme’s various features, including security, are analyzed in detail, and the scheme is deployed and tested on the Hyperledger Fabric and Ethereum blockchain platform. The experiment results demonstrate that the voting scheme satisfies the security requirement, and it has outstanding advantages in performance.

As a fundamental mathematical problem in the field of machine learning, the linear separability test still lacks a theoretically complete and computationally efficient method. This paper proposes and proves a sufficient and necessary condition for linear separability test based on a sphere model. The advantage of this test method is two-fold: (1) it provides not only a qualitative test of linear separability but also a quantitative analysis of the separability of linear separable instances; (2) it has low time cost and is more efficient than existing test methods. The proposed method is validated through a large number of experiments on benchmark datasets and artificial datasets, demonstrating both its correctness and efficiency.

Recently, image crowdsourcing, a new trading mode, has been proposed to bridge the gap between the excess photos generated by intelligent devices and the great demand for images. However, traditional crowdsourcing methods often rely on centralized platforms, which risk data leakage and a single point of failure (SPOF). Moreover, due to the subjectivity of image quality assessment and the complexity of image data structure, image quality is difficult to control for traditional crowdsourcing frameworks without exposing data privacy. In this work, we propose a blockchain-based image crowdsourcing framework named QAIC to address these issues. Within the framework of QAIC, the transaction information is stored using a multichain structure, and the transaction process is implemented using smart contracts. We design an image selection and pricing mechanism for QAIC, where high-quality image sets can be spontaneously selected, and each image can be dynamically priced based on distortion degree and content relevance. Finally, to accurately obtain image quality, we design a dual output neural network model to evaluate the image quality, where a lightweight architecture is adopted, and piecewise outputs are designed to protect image privacy and reduce the on-chain computational cost Extensive analysis and experiments demonstrate that the quality of transaction data and reasonable pricing can be ensured using the QAIC without compromising image privacy.

The current state-of-the-art(SOTA) blind watermark embedding method MBRS based on deep learning is less robust to Crop, and additional diffusion layers need to be added for optimization. However, the diffusion layer will make the model less robust to noise other than Crop. Therefore, MBRS which needs to add or delete components is not a practical watermarking framework. Not only that, MBRS is easy to generate chessboard artifacts, resulting in the generated watermark being easy to be detected by the human eye. Therefore, we construct a more generalized watermarking framework and propose an improved blind watermarking method. The method addresses the shortcomings of MBRS by using an improved message processor with sub-pixel convolution layers and low-frequency features and incorporating double discriminators to improve the performance of the network. Extensive experiments show that our method demonstrates superior results compared to the SOTA method.

In watermarking, the watermark embedding strength is crucial, and the introduction of the strength factor can adjust the trade-off between the quality of the encoded image and the accuracy of the recovered message, thus enabling good imperceptibility and robustness of the encoded image. In traditional watermarking methods, the strength factor is selected in relation to the cover image, and based on different images, different strength factors are manually selected or algorithmically derived to adjust the visual effect of the watermarked image. However, due to the subjectivity and inflexibility of traditional algorithms, they can not achieve the effect of adaptive adjustment of watermarked images. Recently, watermarking methods combined with deep learning have gradually occupied the mainstream of this field. In the testing stage, to balance the overall robustness and imperceptibility, the strength factor is no longer selected based on the cover image as in traditional methods. Instead, it is set to a universal value. Therefore, the watermarking method based on deep learning is still in the primary stage of the trial-and-error method. To solve the subjectivity of the hand-designed embedding strength algorithm of the traditional watermarking methods and the low elasticity of the strength factor of the learning method so as to realize the adaptive embedding of watermarks, we propose an adaptive watermarking method with separate training. The proposed method adds a new component, the Adaptor, compared to other frameworks. The Adaptor can adaptively select strength factors to control the embedding strength of the watermark relying on the cover image and secret message. A two-stage training method is used to maintain the stability of the training and to achieve the best results for each component. With the results obtained from our experiments, our proposed method can find the appropriate strength factor and optimize it, resulting in improved robustness and imperceptibility of the watermark. The proposed method shows better results compared to the current state-of-the-art algorithms.

Data transaction promotes data circulation and mitigates the issues of Data Island. However, unlike traditional physical goods, data can be easily copied. This feature always induces leakage after selling the right of use in the transaction, resulting in the loss of users’ interest. In fact, it is difficult to restrain when buyers make backups and leak after purchasing the complete data. Therefore, it’s necessary to design corresponding schemes to ensure the data security in the transaction process, find the data after the buyer leaked and identify the malicious leaker. In this paper, we propose a data transaction architecture with leakage tracing by blockchain and digital fingerprint. In our architecture, buyers interact with sellers who pass the screening through the smart contracts. The digital fingerprint is utilized to avoid the malicious behavior of sellers and track leaked data by buyers. Then we provide an improvement plan to optimize the scheme of leakage tracing in data transactions by combining reporting and deposit mechanisms. In addition, we develop a Decentralized Application (DApp) to build the model for data transaction and leakage tracing. Experiments on the DApp demonstrate the effectiveness and feasibility of our proposed architecture.

Data has become an integral part of the modern world, and its value has led to the creation of new markets for data trading. Protecting the copyright of trading data and ensuring the fairness of transactions present significant challenges in the current trading market. Therefore, we propose a copyright-preserving data trading scheme based on smart contracts and perceptual hashing, called CPDT, which use smart contracts, combined with perceptual hashing, to prevent malicious acts of illegal reselling and to ensure fair transactions. We have also designed a perceptual hashing algorithm for our trading scheme. Extensive experiments have been conducted to demonstrate the feasibility of the scheme.

In the traditional image copyright protection system, watermarking technology is considered as an important technology to overcome the data protection problem and verify the data ownership relationship. However, the existing watermarking technology often needs a trusted third party to arbitrate in the implementation process, which may be difficult to find or costly. Meanwhile, the common image watermarking algorithm needs to operate the image data, which will inevitably lead to the image data loss. With the development of blockchain technology, the functions of de-trusted third parties and the fair and automatic processing characteristics of smart contracts attached to it have entered the public view. In this paper, we study the function of the zero-watermarking algorithm in image protection and its complete storage and authentication scheme, then propose a secure blockchain-based image copyright protection framework and build a system according to this framework. This framework combines blockchain and zero-watermark technology and uses interplanetary file system to solve the blockchain data expansion problem. Besides, the image owner can authenticate the image and realize the copyright traceability of the image. Furthermore, the keyword search function of the stored images in the system is realized based on a smart contract, which solves the problem of lack of trusted third parties. Experiment illustrates that the proposed scheme is feasible.

Multi-packet reception (MPR) technology provides a means of boosting wireless network capacity without requiring additional spectrum. It has received widespread attention over the past two decades from both industry and academic researchers. Despite the huge promise and considerable attention, provable good algorithms for maximizing network capacity in MPR-capable wireless networks are missing in the state of the art. One major technical obstacle is due to the complicated non-binary nature of the link independence; something which appears intractable with existing graph-theoretic methods. In this paper, we present practical polynomial-time approximation algorithms for variants of capacity optimization problems in MPR-capable wireless networks which achieve constant approximation bounds for the first time ever. In addition, polynomial-time approximation schemes are developed for those variants in wireless networks with constant-bounded MPR capabilities.

Multiple-input multiple-output (MIMO) technology provides a means of boosting network capacity without requiring additional spectrum. It has received widespread attention over the past decade from both industry and academic researchers, now forming a key component of nearly all emerging wireless standards. Despite the huge promise and considerable attention, a rigorous algorithm-theoretic framework for maximizing network capacity in multihop wireless MIMO\ networks is missing in the state of the art. The existing algorithms and protocols for maximizing network capacity in multihop wireless MIMO networks are purely heuristic without any provable performance guarantees. In this paper we conduct a comprehensive algorithm study for maximizing network capacity in multihop wireless MIMO networks with receiver-side interference suppression, including the full characterization of NP-hardness and APX-hardness, the polynomial time approximation schemes, and the practical approximation algorithms with provable performance guarantees.

ABSTRACT This paper presents a learning‐based steganalysis/detection method to attack spatial domain least significant bit (LSB) matching steganography in grayscale images, which is the antetype of many sophisticated steganographic methods. We model the message embedded by LSB matching as the independent noise to the image, and theoretically prove that LSB matching smoothes the histogram of multi‐order differences. Because of the dependency among neighboring pixels, histogram of low order differences can be approximated by Laplace distribution. The smoothness caused by LSB matching is especially apparent at the peak of the histogram. Consequently, the low order differences of image pixels are calculated. The co‐occurrence matrix is utilized to model the differences with the small absolute value in order to extract features. Finally, support vector machine classifiers are trained with the features so as to identify a test image either an original or a stego image. The proposed method is evaluated by LSB matching and its improved version “Hugo”. In addition, the proposed method is compared with state‐of‐the‐art steganalytic methods. The experimental results demonstrate the reliability of the new detector. Copyright © 2013 John Wiley & Sons, Ltd.

Wireless sensor networks become ubiquitous to collect people's information in many people-centric applications, such as, health care, smart space and public safety. Because any misusage of these personal data might result in the leakage of privacy, it is expected that the data requesters can only access to the data what they are entitled to read. Based on a revised hash chain technique, we proposed a novel time-based privacy protection (TPP) scheme for multi-attribute data in WSNs. In the scheme, all the personal data are divided into 2-D subspaces representing data attribute and generation time. Data in each subspace is encrypted with a sub-key before its transmission to the sink. Anyone who wants to read data attribute at a particular time must get the corresponding sub-key from the sender node. TPP can generate a sub-key for data in each subspace in an efficient manner in terms of less sub-key generation time and low memory space usage. The simulation results show that the schemes can be applied to the resource limited WSNs efficiently.