Search Results (27)

The increasing sophistication of cyberattacks on smart grid infrastructure demands advanced anomaly detection and recovery systems that balance high recall rates with acceptable precision while providing reliable data restoration capabilities. This study presents an optimized two-stage anomaly detection and recovery system combining an enhanced TimerXL detector with a DeBERTa-v3-based verification and recovery mechanism. The first stage employs an optimized increment-based detection algorithm achieving 95.0% for recall and 54.8% for precision through multidimensional analysis. The second stage leverages a modified DeBERTa-v3 architecture with comprehensive 25-dimensional feature engineering per variable to verify potential anomalies, improving the precision to 95.1% while maintaining 84.1% for recall. Key innovations include (1) a balanced loss function combining focal loss ($\alpha$ = 0.65, $\gamma$ = 1.2), Dice loss (weight = 0.5), and contrastive learning (weight = 0.03) to reduce over-rejection by 73.4%; (2) an ensemble verification strategy using multithreshold voting, achieving 91.2% accuracy; (3) optimized sample weighting prioritizing missed positives (weight = 10.0); (4) comprehensive feature extraction, including frequency domain and entropy features; and (5) integration of a generative time series model (TimER) for high-precision recovery of tampered data points. Experimental results on 2000 hourly smart grid measurements demonstrate an F1-score of 0.873 ± 0.114 for detection, representing a 51.4% improvement over ARIMA (0.576), 621% over LSTM-AE (0.121), 791% over standard Anomaly Transformer (0.098), and 904% over TimesNet (0.087). The recovery mechanism achieves remarkably precise restoration with a mean absolute error (MAE) of only 0.0055 kWh, representing a 99.91% improvement compared to traditional ARIMA models and 98.46% compared to standard Anomaly Transformer models. We also explore an alternative implementation using the Lag-LLaMA architecture, which achieves an MAE of 0.2598 kWh. The system maintains real-time capability with a 66.6 ± 7.2 ms inference time, making it suitable for operational deployment. Sensitivity analysis reveals robust performance across anomaly magnitudes (5–100 kWh), with the detection accuracy remaining above 88%. Full article

Hybrid-energy vessels offer significant advantages in reducing carbon emissions and air pollutants by integrating traditional internal combustion engines, electric motors, and new energy technologies. However, during operation, the high reliance of hybrid-energy ships on networks and communication systems poses serious data security risks. Meanwhile, the complexity of energy scheduling presents challenges in obtaining feasible solutions. To address these issues, this paper proposes an innovative two-stage security optimization scheduling framework aimed at simultaneously improving the security and economy of the system. Firstly, the framework employs a CNN-LSTM hybrid model (WOA-CNN-LSTM) optimized using the whale optimization algorithm to achieve real-time detection of false data injection attacks (FDIAs) and post-attack data recovery. By deeply mining the spatiotemporal characteristics of the measured data, the framework effectively identifies anomalies and repairs tampered data. Subsequently, based on the improved multi-objective whale optimization algorithm (IMOWOA), rapid optimization scheduling is conducted to ensure that the system can maintain an optimal operational state following an attack. Simulation results demonstrate that the proposed framework achieves a detection accuracy of 0.9864 and a recovery efficiency of 0.969 for anomaly data. Additionally, it reduces the ship’s operating cost, power loss, and carbon emissions by at least 1.96%, 5.67%, and 1.65%, respectively. Full article

With the increasing amount of cloud-based speech files, the privacy protection of speech files faces significant challenges. Therefore, integrity authentication of speech files is crucial, and there are two pivotal problems: (1) how to achieve fine-grained and highly accurate tampering detection and (2) how to perform high-quality tampering recovery under high tampering ratios. Tampering detection methods and tampering recovery methods of existing speech integrity authentication are mutually balanced, and most tampering recovery methods are carried out under ideal tampering conditions. This paper proposes an encrypted speech integrity authentication method that can simultaneously address both of problems, and its main contributions are as follows: (1) A 2-least significant bit (2-LSB)-based dual fragile watermarking method is proposed to improve tampering detection performance. This method constructs correlations between encrypted speech sampling points by 2-LSB-based fragile watermarking embedding method and achieves low-error tampering detection of tampered sampling points based on four types of fragile watermarkings. (2) A speech self-recovery model based on residual recovery-based linear interpolation (R2-Lerp) is proposed to achieve tampering recovery under high tampering ratios. This method constructs the model based on the correlation between tampered sampling points and their surrounding sampling points and refines the scenarios of the model according to the tampering situation of the sampling points, with experimental results showing that the recovered speech exhibits improved auditory quality and intelligibility. (3) A scrambling encryption algorithm based on the Lorenz mapping is proposed as the speech encryption method. This method scrambles the speech sampling points several times through 4-dimensional chaotic sequence, with experimental results showing that this method not only ensures security but also slightly improves the effect of tampering recovery. Full article

In recent years, the advancement of digital image processing technology and the proliferation of image editing software have reduced the technical barriers to digital image processing, enabling individuals without professional training to modify and edit images at their discretion. Consequently, the integrity and authenticity of the original image content assume greater significance. The current techniques for detecting tampering in watermark embedding are inadequate in terms of security, efficiency, and image restoration quality. In light of the aforementioned considerations, this paper puts forth an algorithm for the detection and restoration of tampered images, which employs a chaotic watermark embedding technique. The algorithm employs a chaotic system to establish a mapping relationship between image sub-blocks, thereby ensuring the randomness of the watermark information with respect to the positioning of the original image block and enhancing the security of the algorithm. Furthermore, the detection algorithm utilizes layered tampering detection to enhance the overall accuracy of the detection process and facilitate the extraction of the fundamental information required for image restoration. The restoration algorithm partially designs a weight assignment function to distinguish between the original image block and the main restored image block, thereby enhancing restoration efficiency and quality. The experimental results demonstrate that the proposed algorithm exhibits superior tamper detection accuracy compared to traditional algorithms, and the quality of the restored images is also enhanced under various simulated tamper attacks. Full article

The verification of image integrity has attracted increasing attention. Irreversible algorithms embed fragile watermarks into cover images to verify their integrity, but they are not reversible due to unrecoverable loss. In this paper, a new dual tampering detection scheme for reversible image fragile watermarking is proposed. The insect matrix reversible embedding algorithm is used to embed the watermark into the cover image. The cover image can be fully recovered when the dual-fragile-watermarked images are not tampered with. This study adopts two recovery schemes and adaptively chooses the most appropriate scheme to recover tampered data according to the square errors between the tampered data and the recovered data of two watermarked images. Tampering coincidence may occur when a large region of the fragile-watermarked image is tampered with, and the recovery information corresponding to the tampered pixels may be missing. The tampering coincidence problem is solved using image-rendering techniques. The experimental results show that the PSNR value of the watermarked image obtained using our scheme can reach 46.37 dB, and the SSIM value is 0.9942. In addition, high-accuracy tampering detection is achieved. Full article

To protect the security of medical images and to improve the embedding ability of data in encrypted medical images, this paper proposes a permutation ordered binary (POB) number system-based hiding and authentication scheme for medical images, which includes three parts: image preprocessing, double hiding, and information extraction and lossless recovery. In the image preprocessing and double hiding phase, firstly, the region of significance (ROS) of the original medical image is segmented into a region of interest (ROI) and a region of non-interest (RONI). Then, the bit plane of the ROI and RONI are separated and cross-reorganization to obtain two new Share images. After the two new Share images are compressed, the images are encrypted to generate two encrypted shares. Finally, the embedding of secret data and attaching of authentication bits in each of these two encrypted shares was performed using the POB algorithm. In the information extraction and lossless recovery phase, the POBN algorithm is first used to extract the authentication bits to realize image tamper detection; then, the embedded secret message is extracted, and the original medical image is recovered. The method proposed in this research performs better in data embedding and lossless recovery, as demonstrated by experiments. Full article

Color face images are often transmitted over public channels, where they are vulnerable to tampering attacks. To address this problem, the present paper introduces a novel scheme called Authentication and Color Face Self-Recovery (AuCFSR) for ensuring the authenticity of color face images and recovering the tampered areas in these images. AuCFSR uses a new two-dimensional hyperchaotic system called two-dimensional modular sine-cosine map (2D MSCM) to embed authentication and recovery data into the least significant bits of color image pixels. This produces high-quality output images with high security level. When tampered color face image is detected, AuCFSR executes two deep learning models: the CodeFormer model to enhance the visual quality of the recovered color face image and the DeOldify model to improve the colorization of this image. Experimental results demonstrate that AuCFSR outperforms recent similar schemes in tamper detection accuracy, security level, and visual quality of the recovered images. Full article

The structure of the grain-and-oil-food-supply chain has the characteristics of complexity, cross-regionality, a long cycle, and numerous participants, making it difficult to maintain the safety of supply. In recent years, some phenomena have emerged in the field of grain procurement and sale, such as topping the new with the old, rotating grains, the pressure of grades and prices, and counterfeit oil food, which have seriously threatened grain-and-oil-food security. Blockchain technology has the advantage of decentralization and non-tampering Therefore, this study analyzes the characteristics of traceability data in the grain-and-oil-food-supply chain, and presents a blockchain-based traceability model for the grain-and-oil-food-supply chain. Firstly, a new method combining blockchain and machine learning is proposed to enhance the authenticity and reliability of blockchain source data by constructing anomalous data-processing models. In addition, a lightweight blockchain-storage method and a data-recovery mechanism are proposed to reduce the pressure on supply-chain-data storage and improve fault tolerance. The results indicate that the average query delay of public data is 0.42 s, the average query delay of private data is 0.88 s, and the average data-recovery delay is 1.2 s. Finally, a blockchain-based grain-and-oil-food-supply-chain traceability system is designed and built using Hyperledger Fabric. Compared with the existing grain-and-oil-food-supply chain, the model constructed achieves multi-source heterogeneous data uploading, lightweight storage, data recovery, and traceability in the supply chain, which are of great significance for ensuring the safety of grain-and-oil food in China. Full article

The Internet is a conduit for vast quantities of digital data, with the transmission of images being especially prevalent due to the widespread use of social media. However, this popularity has led to an increase in security concerns such as image tampering and forgery. As a result, image authentication has become a critical technology that cannot be overlooked. Recently, numerous researchers have focused on developing image authentication techniques using deep learning to combat various image tampering attacks. Nevertheless, image authentication techniques based on deep learning typically classify only specific types of tampering attacks and are unable to accurately detect tampered images or indicate the precise location of tampered areas. The paper introduces a novel image authentication framework that utilizes block-wise encoding through Variational Autoencoder and Generative Adversarial Network models. Additionally, the framework includes a classification mechanism to develop separate authentication models for different images. In the training phase, the image is first divided into blocks of the same size as training data. The goal is to enable the model to judge the authenticity of the image by blocks and to generate blocks similar to the original image blocks. In the verification phase, the input image can detect the authenticity of the image through the trained model, locate the exact position of the image tampering, and reconstruct the image to ensure the ownership. Full article

The demand for using multimedia network infrastructure for transmission grows with each passing day. Research scholars continue to develop new algorithms to strengthen the existing network security framework in order to ensure the privacy protection and content authentication of multimedia content and avoid causing huge economic losses. A new technology for multimedia image copyright protection and content authentication has been proposed. The innovations lie in the use of an inter-block coefficient difference algorithm to embed robust watermarking in the transform domain, and the same fragile watermark is embedded twice in the spatial domain so that any tiny tampering can be identified and located. A new encryption algorithm combined with Arnold transform is used to encrypt data before embedding. However, some security vulnerabilities were found, and successful cryptanalysis and attack were conducted. Subsequently, an improved scheme was proposed to improve the security and tamper detection ability of the original watermarking scheme and recover the tampered robust watermark. The results show that the improved scheme is safer and more reliable and shows good performance in tampering detection and the recovery robustness of the watermark. Full article

With the rapid development of digital signal processing tools, image contents can be easily manipulated or maliciously tampered with. Fragile watermarking has been largely used for content authentication purposes. This article presents a new proposal for image fragile watermarking algorithms for tamper detection and image recovery. The watermarked bits are obtained from the parity bits of an error-correcting code whose message is formed from a binary chaotic sequence (generated from a secret key known to all legitimate users) and from bits of the original image. Part of the codeword (the chaotic bits) is perfectly known to these users during the extraction phase, adding security and robustness to the watermarking method. The watermarked bits are inserted at specific sub-bands of the discrete wavelet transform of the original image and are used as authentication bits for the tamper detection process. The imperceptibility, detection, and recovery of this algorithm are tested for various common attacks over digital images. The proposed algorithm is analyzed for both grayscale and colored images. Comparison results reveal that the proposed technique performs better than some existing methods. Full article

Digital images have unique features that include being both easily transmittable over the Internet and being easy to tamper. With the advancement of digital processing techniques and an increasing number of valuable digital images being transmitted via the Internet, image authentication has been made more crucial than ever. In this paper, we present an image authentication scheme with tamper localization and self-recovery using fragile watermarking. We embed the fragile watermarks consisting of the authentication code and the recovery information onto the image to verify its integrity. The proposed fragile watermarking scheme can authenticate the image without accessing the original image, localizing the modifications as well as verifying the integrity, and even reconstructing the tampered regions. We use an AMBTC compressed code as the authentication code to minimize the distortion introduced by embedding. To reduce the blocking effect that occurs in the reconstructed image, a VQ compressed code is applied instead of the average intensity as the recovery information. Several representative test images and 200 different test images were randomly selected from BOWS to examine the performance of the proposed scheme. Experimental results confirm that the proposed scheme can effectively resist a cutting attack and a copy-paste attack while retaining the high accuracy of tamper localization. The average TPR and average FTP rate were around 97% and 0.12%, respectively, while maintaining the image quality of the watermarked image and restoring the image at up to 48 dB and 39.28 dB, respectively. Full article

A Fault Attack (FA) is performed mainly under the data corruption model and poses a threat to security chips. Instruction corruption can enact the same purpose at the behavioral level, which is produced by interfering with the instruction system. Laser Fault Injection (LFI) on program memory during the instruction-fetching process, which we refer to as an instruction-fetching attack, is studied in this paper. This process bears the ability to produce a controllable instruction-fetching fault. Our work shows the implementation of the attack and its specific application case on an 8-bit microcontroller. The main contributions of this paper include: (1) We have mapped the sensitive areas precisely to the faulted instructions via laser injection and implemented controllable instruction tampering. (2) A Collision Fault Attack (CFA) scheme based on instruction-fetching fault is proposed. (3) The impacts of the faulted instructions are fully explored, including the influence on subsequent operations and key recovery. (4) The fault mechanism of the on-chip Flash is further investigated. Instruction-fetching fault means that the controller fetches a tampered instruction from the program memory under external interference, which likely gives rise to an invalid or incorrect operation. The experiment confirms that this specific fault can induce particular types of faults that are different to realize, e.g., the byte-fault model in CFA. The realization, application and mechanism of instruction-fetching fault are discussed in detail. Full article

Resilient manufacturing is a vision in the Industry 5.0 blueprint for satisfying sustainable development goals under pandemics or the rising individualized product needs. A resilient manufacturing strategy based on the Industrial Internet of Things (IIoT) networks plays an essential role in facilitating production and supply chain recovery. IIoT contains confidential data and private information, and many security issues arise through vulnerabilities in the infrastructure. The traditional centralized IIoT framework is not only of high cost for system configuration but also vulnerable to cyber-attacks and single-point failure, which is not suitable for achieving the resilient manufacturing vision in Industry 5.0. Recently, researchers are seeking a secure solution of middleware based on blockchain technology integration for decentralized IIoT, which can effectively protect the consistency, integrity, and availability of IIoT data by utilizing the auditing and tamper-proof features of the blockchain. This paper presented a review of secure blockchain middleware for decentralized IIoT towards Industry 5.0. Firstly, the security issues of conventional IIoT solutions and the advantages of blockchain middleware are analyzed. Secondly, an architecture of secure blockchain middleware for decentralized IIoT is proposed. Finally, enabling technologies, challenges, and future directions are reviewed. The innovation of this paper is to study and discuss the distributed blockchain middleware, investigating its ability to eliminate the risk of a single point of failure via a distributed feature in the context of resilient manufacturing in Industry 5.0 and to solve the security issues from traditional centralized IIoT. Also, the four-layer architecture of blockchain middleware presented based on the IIoT application framework is a novel aspect of this review. It is expected that the paper lays a solid foundation for making IIoT blockchain middleware a new venue for Industry 5.0 research. Full article

Embedded systems are increasingly applied in numerous security-sensitive applications, such as industrial controls, railway transports, intelligent vehicles, avionics and aerospace. However, embedded systems are compromised in the execution of untrusted programs, where the instructions could be maliciously tampered with to cause unintended behaviors or program execution failures. Particularly for remote-controlled embedded systems, program execution monitoring and instruction fault self-repair are important to avoid unintended behaviors and execution interruptions. Therefore, this paper presents a hardware-enhanced embedded system with the integration of a Security Processing Unit (SPU) in which integrity signature checking and checkpoint-rollback mechanisms are coupled to achieve real-time program execution monitoring and instruction fault self-repairing. This System-on-Chip (SoC) design was implemented and validated on the Xilinx Virtex-5 FPGA development platform. Based on the evaluation of the SPU in terms of the performance overhead, security capability, and resource consumption, the experimental results show that, while the CPU executes different benchmarks, the average performance overhead of the SPU lowers to 1.92% at typical 8-KB I/D caches, and it provides both program monitoring and fault self-repairing capabilities. Unlike conventional hardware detection technologies that require manual handling to recovery program executions, the CPU–SPU collaborative SoC is a resilient architecture equipped with instruction tampering detection and a post-detection strategy of instruction fault self-repairing. Moreover, the embedded system satisfies a good balance between high security and resource consumption. Full article