With the rapid advancement of the intelligent Internet of Vehicles (IoV), accurate traffic sign classification is essential to ensure driving safety and improve environmental perception. However, conventional image classification models often rely on local features and spatial domain processing, lacking global context modeling and facing computational limitations. To address these challenges, this paper proposes a quantum discrete Fourier transform-based hierarchical self-attention Vision Transformer (QHSA-ViT). Using the parallelism and high-dimensional feature extraction capabilities of quantum computing, the proposed model enhances the quality and efficiency of representation. Specifically, a quantum frequency domain feature representation (QFDFR) module based on a quantum discrete Fourier transform (QDFT) is introduced to capture rich spectral features, while a quantum self-attention fusion (QSAF) module built on a linear combination of unitaries (LCU) and generalized quantum singular value transformation (GQSVT) integrates multilevel attention. The experimental results on five benchmark datasets, including GTSRB, show that QHSA-ViT outperforms baseline models with an average improvement of 9.01% in accuracy and 8.48% in the F1 score. These results validate the effectiveness of the proposed model and highlight its practical applicability and scalability for understanding traffic scenes in intelligent IoV.

Smart healthcare aims to revolutionize medical services by integrating artificial intelligence (AI). The limitations of classical machine learning include privacy concerns that prevent direct data sharing among medical institutions, untimely updates, and long training times. To address these issues, this study proposes a digital twin-assisted quantum federated learning algorithm (DTQFL). By leveraging the 5G mobile network, digital twins (DT) of patients can be created instantly using data from various Internet of Medical Things (IoMT) devices and simultaneously reduce communication time in federated learning (FL) at the same time. DTQFL generates DT for patients with specific diseases, allowing for synchronous training and updating of the variational quantum neural network (VQNN) without disrupting the VQNN in the real world. This study utilized DTQFL to train its own personalized VQNN for each hospital, considering privacy security and training speed. Simultaneously, the personalized VQNN of each hospital was obtained through further local iterations of the final global parameters. The results indicate that DTQFL can train a good VQNN without collecting local data while achieving accuracy comparable to that of data-centralized algorithms. In addition, after personalized training, the VQNN can achieve higher accuracy than that without personalized training.

The development of supply chain systems demands optimization of various technologies in terms of efficiency and resource conservation. As an emerging technology, cross-chain technology in blockchain aims to achieve interoperability and resource sharing between different blockchain networks, enhancing data liquidity and system efficiency. However, relay chains in cross-chain interactions require storing a large number of transaction records, leading to excessive communication and storage loads, which can cause network performance degradation, storage resource exhaustion, and low transaction processing efficiency. To address these issues, this paper proposes a supply chain system-driven efficient quantum blockchain cross-chain transaction scheme (SCS-QBCT). Firstly, SCS-QBCT uses the quantum Fourier transform (QFT) to convert transaction records on relay chains from the time domain to the frequency domain, reducing data redundancy and significantly lowering storage space consumption. Secondly, a multifunctional smart contract, designed to include conventional functions, enables value transfer, transaction withdrawal, transaction query, node identity management, and transaction type identification. Furthermore, inverse quantum Fourier transform (IQFT) is used to restore quantum state transaction records in blocks to classical records, supporting transaction query requests. Finally, the experimental results and theoretical analysis show that SCS-QBCT performs excellently in reducing storage consumption, improving system efficiency, practicality, and security, and meeting the optimization goals of supply chain systems.

Traffic sign recognition is essential for autonomous driving, enhancing driving efficiency and ensuring road safety. Traffic signs utilize colors and patterns to relay critical information, with color accuracy being especially significant. Traditional models, reliant on extensive data and computing resources, struggle to meet the efficiency demands of electric vehicles with carbon-intelligent computing. Leveraging the physical properties of quantum superposition states, quantum computing offers a solution with its unique parallel computing capabilities, potentially enhancing recognition efficiency and facilitating real-time processing. Quantum convolutional neural networks (QCNNs) show promise in processing large-scale image data with improved efficiency and accuracy. However, most of QCNN researches focus on grayscale image classification, with limited studies on multichannel data. This article introduces the quantum channel attention convolutional neural network (QCACNN), which incorporates a quantum channel attention layer (QCAL) to enhance multichannel data classification. The experimental results demonstrate that QCACNNs surpasses traditional QCNNs in traffic sign recognition, performing comparably to conventional CNNs and SENet models with fewer computing resources. Detailed performance analysis and ablation studies validate the effectiveness of each component within this architecture. Quantum noise resistance tests confirm the robustness of QCACNN, making it a viable solution for electric vehicles with enhanced scalability.

Federated learning (FL) is a privacy-preserving alternative to centralized machine learning, where model training is performed on local devices and only global model updates are shared, effectively addressing challenges, such as data silos and privacy protection. Recently, quantum FL (QFL), an emerging FL branch, has garnered significant attention in many industry applications. However, existing QFL algorithms predominantly employ average weighting for global model training, which shows poor performance on heterogeneous healthcare data. To address this challenge, this study proposes a dynamic aggregation QFL algorithm (DAQFL) for intelligent diagnosis. Specifically, it utilizes quantum neural networks (QNNs) as local training models and designs corresponding variational quantum circuits (VQC). To mitigate performance degradation caused by the heterogeneity of medical industrial data, a dynamic aggregation method based on accuracy is proposed to enhance global model performance effectively. Extensive experiments with three distribution settings, including independent and identically distributed (IID), non-independent and identically distributed (Non-IID), and long-tail datasets, show that DAQFL outperforms baseline algorithms in accuracy and training speed. It also performs well in privacy protection and robustness of anti-noise, improving its suitability for real-world medical applications.

In recent years, more and more scholars have begun to focus on sentiment analysis on social media. Current sentiment analysis collects all relevant data, including public thoughts, opinions, and feelings, from a variety of open sources. In addition, it automatically predicts different aspects of outcomes or trends based on information collected globally in real time. This research area explores how to extract sentiment information from different modalities (e.g., text, images, and audio). However, the currently existing techniques face several challenges. It is difficult to achieve effective interaction with completely heterogeneous data, and these techniques cannot adequately guarantee data security during data interaction, which is particularly important when dealing with sensitive information. Therefore, this article introduces existing methods for protecting data privacy. Based on this foundation, we propose a novel algorithm called collaborative dynamic hierarchical federated learning with hypernetwork aggregation (CDHFL-HA), which is suitable for sentimental analysis. CDHFL-HA ensures that the data remain local to each participant while leveraging the data similarity between participants on the server and processing interference data on the participant to enhance the accuracy of the current sentimental analysis. In addition, an essential aspect considered in the proposed algorithm is explainability. Understanding the decisions and predictions made by sentiment analysis models is crucial for gaining trust and acceptance in real-world applications. CDHFL-HA incorporates explainability features, providing insights into the decision-making process, thus enhancing the interpretability of sentiment analysis results. Numerous experimental results show that the algorithm outperforms existing algorithms in complex scenarios, with a minimum accuracy of 0.6007 and a maximum of 0.9962. In addition, it can be seen from the experimental results in this article, that the communication parameters in the experiments are similar to those of other federated learning, while the number of training rounds is improved by up to 50% (i.e., 20 rounds faster) relative to other algorithms.

Image descriptors are crucial in remote sensing image matching tasks. However, the presence of nonlinear transformation and dimensional collapse inherent in the perspective imaging process often poses challenges to achieving accurate matches. Existing descriptors lack a theoretical analysis of the perspective distortion process and fail to mine the patterns hidden in the perspective imaging process, consequently limiting their efficacy in remote sensing image matching. To uncover the underlying patterns in the image and devise a perspective-invariant descriptor, this paper proposes a perspective-invariant descriptor network (PIDNet). In our approach, we first analyze the remote sensing imaging process and demonstrate that it can be described in a new, conceptually simple linear space named the perspective distortion space. Second, we extract the bases from this space via the intersection-over-union (IoU) metric. As a result, each element in the space can be linearly expressed by the bases. Finally, we utilize these bases to design and learn a perspective-invariant descriptor. The core idea of our descriptor is based on the fact that each base corresponds to a unique imaging viewpoint. Therefore, any imaging viewpoint can be linearly represented as a combination of the bases. To implement our PIDNet, we propose a perspective sampling network module (PSNM) based on the spatial transform networks (STN) since no modules are available for our image sampling process. Furthermore, we introduce a perspective convolutional layer (PCLayer) to extract intermediate covariant features. Then, we concatenate the covariant features to learn a perspective-invariant descriptor. Experimental results on three datasets, including single-modal and multi-modal images, demonstrate the superior performance of PIDNet compared to state-of-the-art methods. Our source code will be publicly available at PIDNet.

With the advent of the intelligent information processing era, more and more private sensitive data are being collected and analyzed for intelligent decision making tasks. Such information processing also brings many challenges with existing privacy protection algorithms. On the one hand, the algorithms based on data encryption compromise the integrity of the original data or incur high computational and communication costs to some extent. On the other hand, algorithms based on distributed learning require frequent sharing of parameters between different computing nodes, which poses risks of leaking local model information and reducing global learning efficiency. To mitigate the impact of these issues, a quantum fuzzy federated learning (QFFL) algorithm is proposed. In the QFFL algorithm, a quantum fuzzy neural network is designed at the local computing nodes, which enhances data generalization while preserving data integrity. In global model, QFFL makes predictions through the quantum federated inference (QFI). QFI leads to a general framework for quantum federated learning on non-independent and identically distributed (IID) data with one-shot communication complexity, achieving privacy protection of local data and accelerating the global learning efficiency of the algorithm. The experiments are conducted on the COVID-19 and MNIST datasets, and the results indicate that QFFL demonstrates superior performance compared to the baselines, manifesting in faster training efficiency, higher accuracy, and enhanced security. In addition, based on the fidelity experiments and related analysis under four common quantum noise channels, the results demonstrated that it has good robustness against quantum noises, proving its applicability and practicality.

Federated learning (FL)-based intrusion detection systems (IDSs) for vehicle road cooperation have attracted significant attention in recent years. However, the nonindependent and identically distributed (Non-IID) data across different edge devices, coupled with uneven computational power, impedes the detection accuracy and efficiency of FL-based IDSs deployed on such devices. To address these issues, this study introduces an efficient IDS to protect privacy of vehicle users using an improved residual network and a novel FL framework, named FEDSA-ResnetV2. First, the improved residual network SA-ResnetV2 is proposed, which adopts a core submodule named SA-ResnetV2-depthwise (SRD) that integrates the advantages of MobileNetV2, ShuffleNet V2, and Self-Attention mechanism for capturing key features of traffic data to effectively detect attacks. Second, the novel FL framework is built, which selects the optimal model globally to enhance detection accuracy, and integrates 8-bit post-quantization technique for resource-efficient edge deployment. To further improve FL training efficiency, an early stopping mechanism is employed. Experimental results show that FEDSA-ResnetV2 outperforms 11 recent Non-FL baselines and 3 state-of-the-art FL baselines in detection performance on three data sets, achieving a maximum performance increase of up to 34%. In terms of execution efficiency, it surpasses the optimal baseline in inference time, model size, mFlops, and parameters, reducing them by up to 72.6 times.

Security and privacy are issues that cannot be ignored when collecting and processing medical data in the Internet of Medical Things (IoMT). The blockchain technology is a decentralized ledger system that has diverse application scenarios in the medical field. The blockchain technology relies on traditional cryptography to ensure data integrity and verifiability, but the creation of quantum computing has made it possible to break traditional encryption and signature methods. Therefore, quantum blockchain can provide a higher level of security for handling medical data. This article innovatively designs a new medical data processing system based on quantum blockchain (QB-IMD). In QB-IMD, a quantum blockchain structure and a novel electronic medical record algorithm (QEMR) are proposed to ensure that the processed data is legitimate and tamper-proof. QEMR combines quantum signature and quantum identity authentication to avoid the potential security risks of digital signatures. In addition, through delegated computing by quantum cloud, medical diagnostic data can be computed without leaking to quantum cloud servers, thus protecting user privacy. Through mathematical proof, theoretical analysis, and simulation, it is demonstrated that our scheme can resist six attacks and is feasible to protect user privacy.

The class imbalance of electrocardiogram (ECG) data is a serious impediment to the development of diagnostic systems for heart disease. To address this issue, this paper proposes HQ-DCGAN, a hybrid quantum deep convolutional generative adversarial network, specifically designed for the generation of ECGs. The proposed algorithm employs different quantum convolutional layers for the generator and discriminator as feature extractors and utilizes parameterized quantum circuits (PQCs) to enhance computational capabilities, along with the model-feature mapping process. Moreover, this algorithm preserves the nonlinearity and scalability inherent to classical convolutional neural networks (CNNs), thereby optimizing the utilization of quantum resources, and ensuring compatibility with contemporary quantum devices. In addition, this paper proposes a novel evaluation metric, 1D Fréchet Inception Distance (1DFID), to assess the quality of the generated ECG signals. Simulation experiments show that HQ-DCGAN exhibits strong performance in ECG signal generation. Furthermore, the generated signals achieve an average classification accuracy of 82.2%, outperforming the baseline algorithms. It has been experimentally proven that HQ-DCGAN is friendly to currently noisy intermediate-scale quantum (NISQ) computers, in terms of both number of qubits and circuit depths, while improving the stability.

Surficial sediment sampling is a necessary operation in deep-sea sediment studies. However, the current approach to selecting sampling locations lacks clarity and definitive guidelines. To solve this problem, we propose an innovative classification-based guided sampling method. Our method leverages the selfdeveloped high-frequency submersible sub-bottom profiler (HF-SSBP) to capture high-resolution SBP images of surficial sediments. Building upon the convolutional neural network - bi-directional long short-term memory (CNN-BLSTM) model, we fuse acoustic faces specific to high-resolution SBP images for classification purposes. The classification results effectively distinguish the various sediment structures, particularly with minor variations. The precision of the model exceeds 95%. We can significantly reduce redundancy in sampling similar sedimentary structures by implementing this method. This approach allows us to maximize the collection of high-value samples in the face of limited sampling conditions. We validated the feasibility of the approach using high-resolution SBP data of surficial sediments in the South China Sea continental slope.

Electrocardiogram (ECG) is the main criterion for arrhythmia detection. As a means of identification, ECG leakage seems to be a common occurrence due to the development of the Internet of Medical Things. The advent of the quantum era makes it difficult for classical blockchain technology to provide security for ECG data storage. Therefore, from the perspective of safety and practicality, this article proposes a quantum arrhythmia detection system called QADS, which achieves secure storage and sharing of ECG data based on quantum blockchain technology. Furthermore, a quantum neural network is used in QADS to recognize abnormal ECG data, which contributes to further cardiovascular disease diagnosis. Each quantum block stores the hash of the current and previous block to construct a quantum block network. The new quantum blockchain algorithm introduces a controlled quantum walk hash function and a quantum authentication protocol to guarantee legitimacy and security while creating new blocks. In addition, this article constructs a hybrid quantum convolutional neural network called HQCNN to extract the temporal features of ECG to detect abnormal heartbeats. The simulation experimental results show that HQCNN achieves an average training and testing accuracy of 94.7% and 93.6%. And the detection stability is much higher than classical CNN with the same structure. HQCNN also has certain robustness under the perturbation of quantum noise. Besides, this article demonstrates through mathematical analysis that the proposed quantum blockchain algorithm has strong security and can effectively resist various quantum attacks, such as external attacks, Entanglement-Measure attack and Interception-Measurement-Repeat attack.

No system entity within a contemporary distributed cyber system can be entirely trusted. Hence, the classic centralized trust management method cannot be directly applied to it. Blockchain technology is essential to achieving decentralized trust management, its consensus mechanism is useful in addressing large-scale data sharing and data consensus challenges. Herein, an n-party quantum detectable Byzantine agreement (DBA) based on the GHZ state to realize the data consensus in a quantum blockchain is proposed, considering the threat posed by the growth of quantum information technology on the traditional blockchain. Relying on the nonlocality of the GHZ state, the proposed protocol detects the honesty of nodes by allocating the entanglement resources between different nodes. The GHZ state is notably simpler to prepare than other multi-particle entangled states, thus reducing preparation consumption and increasing practicality. When the number of network nodes increases, the proposed protocol provides better scalability and stronger practicability than the current quantum DBA. In addition, the proposed protocol has the optimal fault-tolerant found and does not rely on any other presumptions. A consensus can be reached even when there are n−2 traitors. The performance analysis confirms viability and effectiveness through exemplification. The security analysis also demonstrates that the quantum DBA protocol is unconditionally secure, effectively ensuring the security of data and realizing data consistency in the quantum blockchain.

Traffic congestion prediction (TCP) plays a vital role in intelligent transportation systems due to its importance of traffic management. Methods for TCP have emerged greatly with the development of machine learning. However, TCP is always a challenging work due to the dynamic characteristics of traffic data and the complex structure of traffic network. This paper presents a new quantum algorithm that can capture temporal and spatial features of traffic data simultaneously for TCP. The algorithm consists of the following steps. First, we give a closed-form solution in the Schrödinger approach theoretically to analyze this TCP problem in time dimension. Then we can get the temporal features from the solution. At last, we construct a quantum graph convolutional network and apply temporal features into it. Thus, the temporal-spatial quantum graph convolutional neural network is proposed. The feasibility of this method is proved through experiments on the simulation platform. The experimental results show the average error rate is 0.21 and can resist perturbation effectively.

With the rapid development of the Internet of Things (IoT), it becomes convenient to use mobile devices to remotely monitor the physiological signals (e.g., Arrhythmia diseases) of patients with chronic diseases [e.g., cardiovascular diseases (CVDs)]. High classification accuracy of interpatient electrocardiograms is extremely important for diagnosing Arrhythmia. The Supraventricular ectopic beat (S) is especially difficult to be classified. It is often misclassified as Normal (N) or Ventricular ectopic beat (V). Class imbalance is another common and important problem in electronic health (eHealth), as abnormal samples (i.e., samples of specific diseases) are usually far less than normal samples. To solve these problems, we propose a sustainable deep learning-based heart beat classification system, called BeatClass. It contains three main components: two stacked bidirectional long short-term memory networks (Bi-LSTMs), called Rist and Morst, and a generative adversarial network (GAN), called MorphGAN. Rist first classifies the heartbeats into five common Arrhythmia classes. The heartbeats classified as S and V by Rist are further classified by Morst to improve the classification accuracy. MorphGAN is used to augment the morphological and contextual knowledge of heartbeats in infrequent classes. In the experiment, BeatClass is compared with several state-of-the-art works for interpatient arrhythmia classification. The$F1$-scores of classifying N, S, and V heartbeats are 0.6%, 16.0%, and 1.8% higher than the best baseline method. The experimental result demonstrates that taking multiple classification models to improve classification results step-by-step may significantly improve the classification performance. We also evaluate the classification sustainability of BeatClass. Based on different physical signal data sets, a trained BeatClass can be updated to classify heartbeats with different sampling rates. Finally, an engineering application indicates that BeatClass can promote the sustainable development of IoT-based eHealth.

With the development of the Augmented and Virtual Reality (AR/VR) technologies, massive biometric data are collected by different organizations. These data have great significance but also worsen the privacy risks. Electro-CardioGram (ECG)-based Identity Recognition (EIR) is a popular Biometric technology. An ECG record is an internal Biology feature of a person and has time continuity. Thus, compared with traditional Biometric methods like face recognition, EIR may be less vulnerable to attack. We propose an Autoencoder-based EIR system, called Personalized AutoEncoder (PerAE). PerAE maintains a small autoencoder model (called Attention-MemAE) for each registered user of a system. The Attention-MemAE enhances the autoencoder by using a memory module and two attention mechanisms. A user's Attention-MemAE classifies the hearbeats of other users as anomalies. An Attention-MemAE can be updated when the distribution of the user's ECG data is changed. By using personalized autoencoder, PerAE can improve the time efficiency and reduce the memory overhead. It improves the adaptability, scalability, and maintainability of EIR systems. Experiment results show that to train an Attention-MemAE with 90 % identification accuracy for a user, we can just take five minutes to collect the user's ECG data (around 500 heartbeat samples).

Cloud computing paradigm is becoming very popular these days. However, it does not include wireless sensors and mobile phones which are needed to enable new emerging applications such as remote home medical monitoring. Therefore, a combined Cloud-Internet of Things (IoT) paradigm provides scalable on-demand data storage and resilient computation power at the cloud side as well as anytime, anywhere health data monitoring at the IoT side. As both the privacy of personal medical data and flexible data access should be provided,attackers exploit diverse social engineering and technology attacks ways, access to personal privacy information stored in the home medical monitoring cloud, with more and more social engineering attacks.Therefore, the data in the Cloud are always encrypted and access control must be operated upon encrypted data together with being fine-grained to support diverse accessibility. Since a plain combination of encryption before access control is not robust and flexible, we propose a scheme referred to as RoFa, with tailored design. The scheme is introduced in a step-by-step manner. The basic scheme (BaS) makes use of cipher-policy attributes based encryption to empower robustness and flexibility. We further propose an advanced scheme (AdS) to improve the computation efficiency by taking the advantages of proxy-reencryption. AdS can greatly decrease the computation overhead on hospital servers due to operation migration. We finally propose an enhanced scheme (EnS) to protect integrity by using aggregate signature. RoFa describes a general framework to solve the secure requirements, and leaves the flexibility of concrete constructions intentionally. We finally compare the robustness and the flexibility of the proposed schemes by performance analysis.

Video synopsis is a smart solution to fast browsing and retrieval of raw surveillance data, in which tube rearrangement plays a key role. However, conventional methods for tube rearrangement are based on minimizing a global energy function, which is computational intensive and time consuming. In this letter, we propose a novel tube rearrangement strategy for online video synopsis by analyzing collision relationship between tubes. A potential collision graph (PCG) is constructed to represent the tubes and their potential collision relationship. Based on the PCG, tube rearrangement is achieved by filling the tubes into synopsis video in a deterministic way, which decreases computational complexity. Finally, we incorporate the proposed tube rearrangement into an online framework to generate video synopsis and validate its efficiency with extensive experiments.

With the technological evolution in telecommunication networks, performance requirements such as better coverage, higher bandwidth, and lower latency have been pushed to new horizons. However, as a direct result network complexity has increased dramatically over the recent years, and with this complexity manageability has suffered. This paper presents the architecture of the E-Stream project which aims to support Next Generation Operations Support Systems. E-Stream applies dimension reduction, data mining, and recommender system techniques in order to handle very high volumes of management events, identify and predict network incidents, and recommend candidate corrective actions to domain experts in Network Operations Centres.

Currently the volume of telecom network management data is expanding exponentially, mainly due to the explosive growth in the number of communicating devices along with the increase in heterogeneity of the networks. Such scale of data obsoletes the traditional approach of extracting offline analytics from the network traces governed by some pre-defined schemes. In order to increase the efficiency of the Operations Support System (OSS) and gain in-depth understanding of the generic relationship between network entities, the monitoring data needs to undergo large-scale deep analytics processing. In this paper we present i-MagNet, an integrated analytics framework developed with the popular real-time stream processing paradigm Storm. The components of i-MagNet intelligently micro-batch segments of incoming streams to enable high-throughput online analytics of management trace streams. Inter-dependence metrics (temporal and statistical) are exploited to extract contiguous event subsequences, which can then be independently examined as part of a network incident analysis system.

The registration of a forward-looking infrared (FLIR) image and an optical satellite image (visible image) is challenging but important for image-based navigation systems. To solve this problem effectively, a coarse-to-fine matching algorithm is proposed. First, geometric rectification based on the attitude angles and height parameter is carried out to eliminate the distinct rotation and scale discrepancies between the FLIR and visible images. Then, in the fine registration step, the edges of the visible image and rectified infrared image are extracted, and a robust point set registration algorithm which can deal with the similarity transformation distortion is proposed. Finally, the experiments on both the simulated images and real images show that our algorithm can achieve excellent performance in terms of both robustness and accuracy, and the registration precision of real images can be around one pixel.

In the above titled paper (ibid., vol. 9, no. 4, pp.599-603, Jul. 2012), formulas (10) and (12) are incorrect. Their correct forms are presented here.

A contour edge detector combing SUSAN principle and surround suppression is proposed in this paper. Specifically, the operator follows the flow of the Canny edge detector. Firstly, the edge gradient information and modified SUSAN principle are utilized to classify contour edge points and texture edge points approximately. Secondly, surround suppression is applied on the texture edges to suppress them. Finally, contour map is constructed through two hysteresis thresholding procedures. Performance comparison with three other detectors is made and experimental results show that our contour detector performs better.

A novel dual watermark technology based on digital copyright technology is proposed, which, making full use of the advances of the web, stores the correlation information including the keys and the dual watermarks in an XML document. A new image watermarking technology to spread a digital image with copyright protection is realised successfully on the Internet. The arithmetic has very good robustness against the most common, non-malevolent data manipulations, including digital-to-analogue conversion and digital format conversion. Finally, the experimental results confirm that the two watermarks embedded by the proposed algorithm are invisible and robust against commonly used image-processing manipulations such as JPEG compression, adding noise, cropping, and rescaling and soon. The proposed algorithm is shown to provide very good results in term of image imperceptibility too.