Search Results (23)

Reversible data hiding (RDH) is a key technique in secure multimedia applications, enabling the exact recovery of both embedded data and the original cover content. To further enhance security and embedding capacity, this paper proposes a dual-image reversible data hiding (DIRDH) method based on absolute moment block truncation coding (AMBTC). In the proposed scheme, two identical AMBTC-decoded images are exploited as twin covers, and secret bits are adaptively embedded into paired pixels using a variable embedding rate. To ensure data integrity, a lightweight Hash-based Message Authentication Code (HMAC) mechanism is integrated, allowing reliable detection of tampering without additional side information. Experimental results demonstrate that the proposed method achieves high embedding capacity while preserving good visual quality and provides effective authentication against representative tampering cases, including pixel modification, noise addition, and cropping. These contributions highlight the advantages of combining DIRDH with AMBTC, offering a practical and secure solution for high-capacity reversible data hiding. Full article

The advancements in communication and information technologies have substantially enabled the extensive distribution and modification of high-resolution color images. Although this accessibility provides many advantages, it also presents risks related to security. Specifically, when image modification is conducted with malicious intent, exceeding typical artistic or enhancement objectives, it can cause significant moral or economic harm to the image owner. To address this security requirement, this study presents an innovative semi-fragile watermarking algorithm designed specifically for high-resolution color images. The proposed method utilizes Discrete Cosine Transform domain watermarking implemented via Quantization Index Modulation with Dither Modulation. It incorporates several elements, such as convolutional encoding, a denoising convolutional neural network, and a very deep super-resolution neural network. This comprehensive strategy aims to provide ownership verification using a logo watermark, in conjunction with tamper detection and content self-recovery mechanisms. The self-recovery criterion is determined using a thumbnail image, created by downscaling to standard definition and applying JPEG2000 lossy compression. The resultant multifunctional design enhances the overall security of the information. Experimental validation confirms the enhanced imperceptibility, robustness, and capacity of the proposed method. Its efficacy was additionally corroborated through comparative analyses using contemporary state-of-the-art algorithms. Full article

In the digital era where images are easily accessible, concerns about image authenticity and integrity are increasing. To address this, we propose a deep learning-based fragile watermarking method for secure image authentication and content recovery. The method utilizes bottleneck features extracted by the convolutional encoder to carry both authentication and recovery information and employs deconvolution at the decoder to reconstruct image content. Additionally, the Arnold Transform is applied to scramble feature information, effectively enhancing resistance to collage attacks. At the detection stage, block voting and morphological closing operations improve tamper localization accuracy and robustness. Experiments tested various tampering ratios, with performance evaluated by PSNR, SSIM, precision, recall, and F1-score. Experiments under varying tampering ratios demonstrate that the proposed method maintains high visual quality and achieves reliable tamper detection and recovery, even at 75% tampering. Evaluation metrics including PSNR, SSIM, precision, recall, and F1-score confirm the effectiveness and practical applicability of the method. Full article

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 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

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

Physical unclonable functions, embedded in terminal devices, can be used as part of the recovery process of session keys that protect digital files. Such an approach is only valuable when the physical element offers sufficient tamper resistance. Otherwise, error correcting codes should be able to handle any variations arising from aging, and environmentally induced drifts of the terminal devices. The ternary cryptographic protocols presented in this paper, leverage the physical properties of resistive random-access memories operating at extremely low power in the pre-forming range to create an additional level of security, while masking the most unstable cells during key generation cycles. The objective is to reach bit error rates below the 10⁻³ range from elements subjected to drifts and environmental effects. We propose replacing the error correcting codes with light search engines, that use ciphertexts as helper data to reduce information leakage. The tamper-resistant schemes discussed in the paper include: (i) a cell-pairing differential method to hide the physical parameters; (ii) an attack detection system and a low power self-destruct mode; (iii) a multi-factor authentication, information control, and a one-time read-only function. In the experimental section, we describe how prototypes were fabricated to test and quantify the performance of the suggested methods, using static random access memory devices as the benchmark. Full article

In this paper, a fragile watermarking scheme for color image authentication and self-recovery with high tampering rates is proposed. The original image is sub-sampled and divided into non-overlapping blocks, where a watermark used for recovery purposes is generated for each one of them. Additionally, for each recovery watermark, the bitwise exclusive OR (XOR) operation is applied to obtain a single bit for the block authentication procedure. The embedding and extraction process can be implemented in three variants (1-LSB, 2-LSB or 3-LSB) to solve the tampering coincidence problem (TCP). Three, six or nine copies of the generated watermarks can be embedded according to the variant process. Additionally, the embedding stage is implemented in a bit adjustment phase, increasing the watermarked image quality. A particular procedure is applied during a post-processing step to detect the regions affected by the TCP in each recovery watermark, where a single faithful image used for recovery is generated. In addition, we involve an inpainting algorithm to fill the blocks that have been tampered with, significantly increasing the recovery image quality. Simulation results show that the proposed framework demonstrates higher quality for the watermarked images and an efficient ability to reconstruct tampered image regions with extremely high rates (up to 90%). The novel self-recovery scheme has confirmed superior performance in reconstructing altered image regions in terms of objective criteria values and subjective visual perception via the human visual system against other state-of-the-art approaches. Full article