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This paper presents a novel image encryption scheme based on extended large-scale randomization key-stream generator. The basic form of the key-stream generator is presented, and employed in digital image ciphering. The modification of the basic form also, presented, and gives encouraging results in image encryption as compared with classical non-linear stream cipher generators and the basic form. Pixel shuffling is performed via vertical and horizontal permutation. Shuffling is used to expand diffusion in the image and dissipate high correlation among image pixels the sequences generated from all presented generators are introduced to five well-known statistical tests of randomness to judge their randomness characteristic. The ciphered images are tested for their residual intelligibility subjectively. The measures applied to images ciphered by one of the classical key-stream cipher generators (Threshold generator) for the purpose of comparison with the presented key-stream algorithms. Experiments results show that the proposed algorithm achieves the image security. In order to evaluate performance, the proposed algorithm was measured through a series of tests. Experimental results illustrate that the proposed scheme shows a good resistance against brute-force and statistical attacks.

Facing with rapidly increasing demands for analyzing high-order data or multiway data, feature-extracting methods become imperative for analysis and processing. The traditional feature-extracting methods, however, either need to overly vectorize the data and smash the original structure hidden in data, such as PCA and PCA-like methods, which is unfavorable to the data recovery, or cannot eliminate the redundant information very well, such as tucker decomposition (TD) and TD-like methods. To overcome these limitations, we propose a more flexible and more powerful tool, called the multiview principal components analysis (Multiview-PCA) in this article. By segmenting a random tensor into equal-sized subarrays called sections and maximizing variations caused by orthogonal projections of these sections, the Multiview-PCA finds principal components in a parsimonious and flexible way. In so doing, two new operations on tensors, the S -direction inner/outer product, are introduced to formulate tensor projection and recovery. With different segmentation ways characterized by section depth and direction, the Multiview-PCA can be implemented many times in different ways, which defines the sequential and global Multiview-PCA, respectively. These multiple Multiview-PCA take the PCA and PCA-like, and TD and TD-like as the special cases, which correspond to the deepest section depth and the shallowest section depth, respectively. We propose an adaptive depth and direction selection algorithm for the implementation of Multiview-PCA. The Multiview-PCA is then tested in terms of subspace recovery ability, compression ability, and feature extraction performance when applied to a set of artificial data, surveillance videos, and hyperspectral imaging data. All numerical results support the flexibility, effectiveness, and usefulness of Multiview-PCA.

Automatic audio event recognition plays a pivotal role in making human robot interaction more closer and has a wide applicability in industrial automation, control and surveillance systems. Audio event is composed of intricate phonic patterns which are harmonically entangled. Audio recognition is dominated by low and mid-level features, which have demonstrated their recognition capability but they have high computational cost and low semantic meaning. In this paper, we propose a new computationally efficient framework for audio recognition. Audio Bank, a new high-level representation of audio, is comprised of distinctive audio detectors representing each audio class in frequency-temporal space. Dimensionality of the resulting feature vector is reduced using non-negative matrix factorization preserving its discriminability and rich semantic information. The high audio recognition performance using several classifiers (SVM, neural network, Gaussian process classification and k-nearest neighbors) shows the effectiveness of the proposed method., 6 pages, 9 figures, published in IEEE International Conf ICCAS 2014 (Best paper award)

Image enhancement is a basic requirement for any computer vision application for further processing of an image. A common limitation with most of the existing methods, when applied to nearly invisible images, is the loss of color details during the enhancement process. So, a fuzzy c-means clustering-based method for image enhancement is proposed which enhances the perceptually invisible image along with preserving its color and naturalness. In this method, the image pixels are grouped into different clusters and are assigned membership values to those clusters. Based on this membership value, its intensity level is modified in the spatial domain. Modification of the gray levels proportional to the membership values leads to the stretching of the image histogram, similar in shape, to the original histogram. The process results in a very small shift in the mean intensity which preserves the color and brightness-related information of the image. The method enhances the image contrast and maintains the naturalness without introducing any artifacts. The simulation results on standard datasets reflect that the proposed algorithm is superior to many state-of-the-art and traditional methods for perceptually invisible images.

Video watermarking on the dual tree-complex wavelet (DT CWT) domain is shown to be effective to offer high robustness. Existing DT CWT video watermarking schemes tend to use all the coefficients on the high-pass sub-bands of the DT CWT domain for watermark embedding and detection, which lack of investigating the correlations among different sub-bands and fail to explore the stable coefficients for robust watermarking. In this paper, we propose a novel DT CWT video watermarking scheme by exploring the stable coefficients on joint sub-bands. We first extract a set of candidate coefficients by applying block singular value decomposition (SVD) on the DT CWT domain. Then, we simulate the watermark embedding by modifying the candidate coefficients on each sub-band, from which we identity two pairs of strongly correlated sub-bands termed as the joint sub-bands. The watermark is eventually embedded by modifying the candidate coefficients of the joint sub-bands on a level which is adaptively chosen according to the video resolution. During the watermark detection, we identify and extract a set of stable coefficients from the candidate coefficients of the joint sub-bands to verify the ownership of the video. Extensive experiments demonstrate the advantage of our propose scheme over the latest DT CWT based schemes, which also performs better than the existing non-DT CWT transformed domain video watermarking schemes.

JPEG-domain enhancement improves the visual quality of JPEG images by directly manipulating the decoded DCT (discrete cosine transform) coefficients, which inevitably leads to mixed compression and enhancement artifacts. Existing forensic methods that merely consider JPEG artifacts are unsuitable to address such mixed artifacts and hence suffer a considerable performance decline in compression parameter estimation and lack the ability to estimate the enhancement parameter. This work attempts to explore the characterization of the mixed artifacts, and to further estimate both the enhancement and compression parameters of JPEG-domain enhanced images. First, a statistical likelihood function is proposed to characterize the periodicity of DCT coefficients, which can measure how well an enhanced image is de-enhanced back to its JPEG compressed version given the compression and enhancement parameters. The proposed likelihood function reaches its maximum if the parameters match their true values. Then, a forensic method of enhancement detection and parameter estimation is developed based on the proposed likelihood function for two kinds of classical JPEG-domain enhancement. Specifically, JPEG-domain enhanced images are detected by thresholding a scalar feature computed upon the likelihoods, and the enhancement and compression parameters are estimated by locating the maximal likelihood. In addition, mathematical proof of the de-enhancement feasibility is provided. Experimental results demonstrate that the proposed method outperforms the compared methods in both enhancement detection and parameter estimation.

We propose a novel privacy-preserving QoS forecasting approach - Edge-Laplace QoS (QoS forecasting with Laplace noise in mobile Edge environments). Edge-Laplace QoS is able to accurately and efficiently forecast Quality of Service (QoS) of various Web Services, while effectively protecting user privacy in mobile edge environments. We employ an improved differential privacy method to add dynamic disguises to the original QoS data in the edge environment to protect user data privacy. A collaborative filtering method is adopted to retrieve similar users' accessing records based on geographic locations of their accessed servers for QoS forecasting. We conduct a set of experiments using several public network data sets. The results show that the efficiency of Edge-Laplace QoS is superior to traditional forecasting approaches. Edge-Laplace QoS is also validated to be more suitable for edge environments than traditional privacy-preserving approaches.

In this article, the innovation-based linear attack against state estimation in cyber-physical systems is studied. The scenario is considered where a sensor transmits its measurements to a remote estimator via a wireless channel. We propose an attack strategy to worsen the estimation performance of system when a passive detector is adopted. Different from the existing results, the side information is adopted synergistically besides the intercepted data. Meanwhile, the attacker needs to remain stealthy to the false-data detector. We analyze the performance degradation of system under the proposed attack strategy by deriving the remote estimation error covariance recursion. With the stealthiness constraint, the optimal stealthy attack can be obtained by solving a convex optimization problem accordingly. Besides, we provide a specific algorithm to calculate the falsified measurements. Finally, the effectiveness of explored attack strategy is demonstrated via simulation examples.

The human imperceptible adversarial examples crafted by l 0 -norm attacks, which aims to minimize l 0 distance from the original image, thereby misleading deep neural network classifiers into the wrong classification. Prior works of tackling l 0 attacks can neither eliminate perturbed pixels nor improve the performance of the classifier in the recovered low-quality images. To address the issue, we propose a novel method, called space transformation pixel defender (STPD), to transform any image into a latent space to separate the perturbed pixels from the normal pixels. In particular, this strategy uses a set of one-class classifiers, including Isolation Forest and Elliptic Envelope, to locate the perturbed pixels from adversarial examples. The value of the neighboring normal pixels is then used to replace the perturbed pixels, which hold more than half of the votes from these one-class classifiers. We use our proposed strategy to successfully defend against well-known l 0 -norm adversarial examples in the image classification settings. We show experimental results under the One-pixel Attack (OPA), the Jacobian-based Saliency Map Attack (JSMA), and the Carlini Wagner (CW) l 0 -norm attack on CIFAR-10, COVID-CT, and ImageNet datasets. Our experimental results show that our approach can effectively defend against l 0 -norm attacks compared with the most popular defense techniques.

In this paper, we propose a novel image encryption method based on logistic chaotic systems and deep autoencoder. In the encryption phase, first, the plaintext image is randomly scrambled by a logistic chaotic system. Then, the random scrambled image is encoded by a deep autoencoder to generate the ciphertext image. In order to obtain the ciphertext image with uniform distribution, we incorporated the uniform distribution constraint into the training of the deep autoencoder. The resulting ciphertext image contains high randomness, which is critical for an excellent image encryption algorithm. Histogram analysis, information entropy analysis, key space analysis, key sensitivity analysis, correlation analysis, and ablation experiments show that the proposed encryption algorithm can effectively resist attacks and has excellent encryption performance while providing high security.

Scale-invariant feature transform (SIFT) has been successfully used for optical image registration, but it cannot produce satisfying results when directly applied to synthetic aperture radar (SAR) images. In the present study, a novel method is proposed for registration between SAR and optical images. First, candidate keypoints are detected using SIFT algorithm. Then, Canny edge detection algorithm is adopted to remove the edge points which are wrong candidate points. Next, SIFT descriptors are generated from these correct keypoints. Last, the fast library for approximate nearest neighbors (FLANNs) algorithm is applied to search matching points in high-dimensional space. Experimental results show that the proposed approach is significantly more accurate and much faster than the original SIFT algorithm.

Deep-learning-based video processing has yielded transformative results in recent years. However, the video analytics pipeline is energy-intensive due to high data rates and reliance on complex inference algorithms, which limits its adoption in energy-constrained applications. Motivated by the observation of high and variable spatial redundancy and temporal dynamics in video data streams, we design and evaluate an adaptive-resolution optimization framework to minimize the energy use of multi-task video analytics pipelines. Instead of heuristically tuning the input data resolution of individual tasks, our framework utilizes deep reinforcement learning to dynamically govern the input resolution and computation of the entire video analytics pipeline. By monitoring the impact of varying resolution on the quality of high-dimensional video analytics features, hence the accuracy of video analytics results, the proposed end-to-end optimization framework learns the best non-myopic policy for dynamically controlling the resolution of input video streams to globally optimize energy efficiency. Governed by reinforcement learning, optical flow is incorporated into the framework to minimize unnecessary spatio-temporal redundancy that leads to re-computation, while preserving accuracy. The proposed framework is applied to video instance segmentation which is one of the most challenging computer vision tasks, and achieves better energy efficiency than all baseline methods of similar accuracy on the YouTube-VIS dataset., Comment: IEEE Transactions on Multimedia

We propose a secure image encryption method using the combination of spatial surface chaotic system(SSCS) and the improved AES algorithm structure. In this scheme, the key of cryptosystem is obtained from the SSCS, this system has better encryption characteristics and its model structure fits the image exactly, and it is designed for image cryptosystems contrasted with the existing a lot of low-dimensional chaotic maps and couple map lattices. The plain image is encrypted with the improved AES algorithm and by performing each round encryption, the key is generated by SSCS in each round, an improved permutation algorithm(IPA) and a reverse diffusion have been presented. The proposed scheme not only improves the efficiency because of the same key stream is shared, but also increases the diffusion effect which can resist differential attack. The presented scheme provides huge key space to deal with the brute-force attacks using the round keys obtained by SSCS, and also very sensitive to initial values of SSCS and plain image. The results of simulation analysis and performance evaluation show that the presented cryptosystem provides strong security performance and may be used as a candidate for real-time implementations.

In order to improve the security of image information in the information age, we propose an image encryption scheme in this paper, which is achieved by using finite-time cluster synchronization of two-layer complex dynamic networks. Considering the cluster characteristics of nodes in complex networks and the time convergence of synchronization, the method of finite-time cluster synchronization, which plays an important role in information encryption/decryption, is investigated and the influence of stochastic disturbance is taken into consideration as well. A two-layer encryption system is proposed, in which the drive layer is used for encryption, and the response layer is used for decryption. The encryption process can be mainly divided into three steps: scrambling preprocessing, encryption function operation, and diffusion. The encryption scheme uses a symmetric encryption method, and both the encryption keys and the decryption keys are disposable and dynamic, which can increase the complexity and security of the keys. A detailed theoretical analysis for the capability of encryption scheme, which has the advantages of great cipher set, fast encryption and decryption speed, good encryption effect, is provided and the time for information encryption/decryption is controllable and predictable. The availability and practicability of the encryption scheme are verified by simulation results.

Wi-Fi device authentication is crucial for defending against impersonation attacks and information forgery attacks. Most of the existing authentication technologies rely on complex cryptographic algorithms. However, they cannot be supported well on the devices with limited hardware resources. A fine-grained device authentication technology based on channel state information (CSI) provides a noncryptographic method, which uses the CSI fingerprints for authentication since CSI can uniquely identify the devices. But long-term authentication based on CSI fingerprints is a challenging work. First, the CSI fingerprints are environment-sensitive, which means that the local authenticator should be updated to adapt to the changing channel state. Second, the local authenticator trained with old CSI fingerprints is outdated when users reconnect to the network after being offline for a long time, thus, it needs to be retrained in the access phase with new fingerprints. To tackle these challenges, we propose a CSI-based enhancing Wi-Fi device authentication protocol and an authentication framework. The protocol helps to collect new CSI fingerprints for authenticator’s training in access phase and performs the fingerprints’ dispersion analysis for authentication. In the association phase, it provides packet-level authentication and updates the authenticator with valid CSI fingerprints. The authenticator consists of an ensemble of small-scale autoencoders, which has high enough time efficiency for packet-level authentication and authenticator’s update. Experiments show that the accuracy of the framework is up to 98.7%, and the authenticator updating method can help the framework maintains high accuracy.

An image encryption algorithm based on Latin square array, instead of the traditional 2D Freidrich architecture, is proposed here. First, two Latin square matrices are used to scrambling and diffusion of the image on the basis of rows and columns. That is, after completing the scrambling and diffusion of the column, the scrambling and diffusion of the row, following those of column, will not stop until all the rows and columns are encrypted. Second, the entire image is subjected to a diffusion operation opposite to the first step line encryption sequence for a further encryption. This algorithm only carries out one-round encryption operation, but simulation experiment results and security analysis show that the algorithm passes statistical tests such as correlation and information entropy, can resist conventional attacks, and is sensitive to keys, so the algorithm has strong security.

As the greatest effective method of protection of multimedia data is image encryption is extensively used in data hiding, security authentication and content protection. Nevertheless, the security and efficiency are still the key issues of image encryption algorithm. However, the existing image encryption techniques face the issues namely poor resistance to plaintext attacks, simple algorithmic structures and small key space. In order to overcome the problem of traditional techniques, a joint image encryption techniques of continuous raster Scan, Henon Chaotic Map and Chebyshev Chaotic Map are developed for providing a security of images. Initially, the continuous raster is used to scramble the pixels of each blocks, where images are partitioning an image into non overlapping blocks and starting point is randomly selected in Henon Chaotic Map to permute the block. To calculate a random matrix, Chebyshev Chaotic Map is used to generate the sequence based on the secret keys. Finally, bitwise XOR operation between the scrambled image and random matrix is conducted to obtain the encrypted image. The proposed method takes very less time for encryption. Simulation results show that the algorithm is efficient and usable for the security of the image encryption system.

This article presents a simple yet effective algorithm for automatically transferring face colors in portrait videos. We extract the facial features and vectorize the faces in the input video using Poisson vector graphics, which encodes the low-frequency colors as the boundary colors of diffusion curves, and the high-frequency colors as Poisson regions. Then, we transfer the face color of a reference image/video to the first frame of the input video by applying optimal mass transport between the boundary colors of diffusion curves. Next the boundary color of the first frame is transferred to the subsequent frames by matching the curves. Finally, with the original or modified Poisson regions, we render the video using an efficient random-access Poisson solver. Thanks to our efficient diffusion curve matching algorithm, transferring colors for the vectorized video takes less than 1 millisecond per frame. Our method is particularly desired for frequent transfer from multiple references due to its information reuse nature. The simple diffusion curve matching also greatly improves the performance of video vectorization, since we only need to solve an optimization problem for the first frame. Since our method does not require correspondence between the reference image/video and the input video, it is flexible and robust to handle faces with significantly different geometries and postures, which often pose challenges to the existing methods. Moreover, by manipulating Poisson regions, we can enhance or reduce the highlight and contrast so that the reference color can fit into the input video naturally. We demonstrate the efficacy of our method on image-to-video transfer and color swap in videos.

In current steganalysis, relying on a large scale of samples, widely-adopted supervised schemes require the training stage while few studies focus on the design of a training-free unsupervised adaptive detector with high efficiency. To fill the gap, we investigate an adaptive statistical model-based detector designed for detecting JPEG steganography. First, in virtue of hypothesis testing theory, together with the distribution of quantized DCT coefficients, we establish the general framework of the statistical model-based detector. Second, based on the framework, we mainly analyze the performance of the detector relying on the selection of the statistical model, parameters estimation, and less significant payload prediction. Third, to improve the reliability of detection, based on the strategy of assigning weights for DCT channels, the novel adaptive statistical model-based detectors are proposed to aim at detecting JPEG steganography, involving the channel-selected or non-channel-selected algorithm. Extensive experiments highlight the effectiveness of the proposed methodology. Moreover, when detecting JPEG images adopted by two steganographic schemes with the small payload, the experimental results show the Area Under Curve (AUC) of our proposed optimal adaptive detector can achieve as high as 0.9567 and 0.9895 respectively, which are both better than that of non-adaptive detector.

This paper provides an in-depth study and analysis of image processing for graphic design through chaotic encryption combined with a wavelet transform algorithm. Firstly, the traditional Mallat algorithm is optimized; since the mean value of the transform coefficients generated after the wavelet transform of the image is used as the initial value of the chaotic system to iterate, when the image is modified, then the mean value of the wavelet coefficients will also change, and the final iteration comes out as two different sequences using the property that the chaotic system is extremely sensitive to the initial conditions, so the algorithm has a certain sensitivity to tampering and localization effect. The image of the encrypted graphic design is decrypted by the chaos decryption system, and the final image information of the graphic design is obtained. In terms of the security of the graphic design image itself, the complex dynamical properties of chaos are fully utilized to encrypt it, and the algorithm has a good encryption effect after statistical characteristic analysis, attack complexity, difference analysis, adjacent pixel correlation analysis, and key sensitivity analysis. The plaintext image is decomposed in odd-even sequence using the boosting algorithm to get the sequence with an even index and the sequence with an odd index; then, the diffusion algorithm is applied to the two sequences by the prediction and update algorithm, and this process is repeated many times to get the two ciphertext sequences after scrambling, merging these two sequences, and matrixing them to get the ciphertext image. Finally, the testing of the embedded planar designed image revolves around four aspects, namely, image processing, tamper sensitivity, robustness, and imperceptibility, to examine the designed planar designed image system. In the diffusion manipulation, the key matrix for diffusion is obtained using the threshold processing method of the Local Binary Pattern (LBP) algorithm for the matrices generated by the Logistic Chaos system, which improves the randomness of the algorithm. The experimental results verify the effectiveness and security of the algorithm.

Image encryption protects the sensitive and confidential information of images. This protection comes from the combination of secret keys, encryption, and decryption algorithms. Chaotic maps are widely utilized by researchers to perform image encryption. Encryption with chaotic maps is very sensitive to initial conditions. Therefore, it can produce unacceptably low performance if the input parameters are not chosen wisely. This problem is solved by using meta-heuristic techniques in image encryption to obtain the optimized initial conditions. This paper presents a systematic review of metaheuristic algorithms used for image encryption. In this study, an effort is made to highlight the features and different issues of existing metaheuristic-based image encryption techniques. Parallel implementation, Hyper-parameter tuning, Stuck in local optima, Premature convergence, and Computational speed methods are generally used to evaluate metaheuristic-based image encryption techniques. The security parameters of these techniques, such as key space, speed, entropy, Number of Pixel Change Rate, Unified Average Changing Intensity, and correlation coefficient, are also evaluated. Articles from various databases are taken for study. In this systematic review, various research questions are identified and based on their answers, results have been formulated. Based on the study, the future directions are also identified for coming researchers in the field of metaheuristic-based image encryption.

In this paper, a novel separable reversible data hiding in homomorphic encrypted images (RDHEI) using POB number system is proposed. The frame of the proposed RDHEI includes three parties: content owner, data hider, and receiver. The content owner divides original image contents into a series of non-overlapping equal-size 2 × 2 blocks, and encrypts all pixels in each block with the same key. The encryption process is carried out in an additive homomorphism manner. The data hider divides the encrypted images into the same size blocks as the encryption phase, and further categories all of the obtained blocks into two sets according to the corresponding block entropy. The embedding processes of the two sets are performed through utilizing permutation ordered binary (POB) number system. For the set with smaller entropies, all pixels in addition to the first pixel in each block are compressed by the POB number system; for the set with larger entropies, only u LSBs of all pixels are compressed in order to vacate room for embedding. The receiver can conduct image decryption, data extraction, and image reconstruction in a separable manner. Experimental results verify the superiority of the proposed method.

This paper proposes security techniques for detecting and counteracting attacks in Networked Control Systems (NCSs). A partially homomorphic based encryption technique , namely the Paillier scheme, is used to protect the confidentiality of the transmitted data over the network against eavesdropping attacks . An integrated secure and private observer-based controller with guaranteed stability together with a secure and private observer-based detection system has been developed. We start with an encrypted observer. Then, an observer-based encrypted controller is added to stabilize the system. Besides, an encrypted observer-based detection algorithm has been used to detect false data injection attacks . Moreover, in our design, the computations on the observer, the controller, and the decision-maker operate on encrypted data while protecting against eavesdropping attacks. Using digital processors in cyber systems leads to quantized transmitted signals through the network and also to design the observer and controller operations in such a way that they work on quantized data efficiently. Limitations on the parameters of the quantizations and the encryption scheme are given to ensure the stability of the closed-loop system and guarantee reliable bounds on the closed-loop performance. Simulation results using Tennessee Eastman Process demonstrate the effectiveness of the proposed scheme.

Data security plays an increasingly important role in modern telecommunications. The advent of quantum computational processors presents a significant threat to today's widely employed public key encryption algorithms, necessitating the adoption of new approaches to data encryption. Whilst quantum key distribution guarantees unconditional security for cryptographic key exchange in optical communication networks, the data rate is slow (Mbit/s), especially when compared to conventional optical communication. Here we present a highly secure encryption approach in which the encryption key, generated by quantum key distribution at a rate of up to 2.9 Mbit/s, was used to seed physical layer encryption performed using time domain spectral phase encoding (TDSPE). This allowed us to demonstrate encrypted 40 Gbit/s quadrature phase shift keyed data communications over 52.3 km of installed optical fiber, which cannot be eavesdropped using brute force computational attacks. Any attempt to eavesdrop the encrypted signal in the physical layer is highly time-sensitive - the phase states must be measured and decrypted prior to optical signal attenuation, which means that the attack procedure typically needs to be completed within a few milliseconds. This work represents the first example of quantum-enhanced physical layer encryption at realistic optical data rates that is fully secure from brute force computational attacks and the first demonstration of TDSPE using continuous-wave laser source and quadrature phase shift key modulation.

In this paper, a novel triple-image encryption and hiding algorithm is proposed by combining a 2D chaotic system, compressive sensing (CS) and the 3D discrete cosine transform (DCT). First, three grayscale plain images are sparsely represented by the 2D discrete wavelet transform (DWT). The resulting sparse matrices are scrambled twice through index sort scrambling and 3D zigzag scrambling. Then, the measurement matrix generated by the 2D chaotic system is used to compress the scrambled matrices. Finally, the compressed matrices are embedded into a color carrier image using the 3D DCT to obtain a visually meaningful cipher image. It is worth mentioning that most of the existing meaningful encryption algorithms encrypt and embed single images, which cannot meet the requirements of the era of big data. However, in our scheme, three grayscale images are simultaneously encrypted and embedded into a single color carrier image, which, when compared with existing algorithms, greatly improves the encryption efficiency. Moreover, simulation experiments and contrastive analyses show that the proposed visual encryption scheme has excellent visual security, robustness, decryption quality and running efficiency.

In recent decades, image encryption has been a popular and important field of research. The image encryption techniques have been studied thoroughly to ensure the safety of digital images on transmission through the networks. A large range of algorithms for chaotic-based cryptographic systems has been suggested and submitted to enhance the efficiency of the encryption methods. The chaotic map is one technique to guarantee security. The benefits of chaotic image encryption include the fact that it is simple to implement; it has a faster encryption speed, and it is powerful against attacks. Due to their extreme sensitivity to initial conditions, unpredictability, and random-like behaviours, many image encryption systems using chaotic maps have been proposed. This study paper presents a scientific review of many types of researches during the (2014-2020) years that used chaotic with its various types (one-dimensional, multi-dimensional, or hyper-chaotic) to process the digital images in the encryption stage or the scrambling phase. Furthermore, it presents a future reading of researches that has a wider role in developing the cryptography field by improving the efficiency of Algorithms where using a chaotic map with other methods gives better results than using chaotic alone in scrambling and encryption methods.

This paper proposes a novel method for secure image encryption and authentication based upon fuzzy extractor driven Elliptic Curve key exchange scheme. Use of fuzzy extractor facilitates both parties to authenticate each other without disclosing their original biometric information. The image encryption is achieved using a shared key in two steps. In the first step, we use a novel Cosine Transform based Chaos System to generate a pseudo- random chaos sequence over a dynamic range. In the second step, this chaos sequence along with Elliptic curve-based encryption is used in code block cipher mode to get the cipher image. To authenticate the encrypted image, an easily computable and verifiable digital signature scheme based on fuzzy extractor and Elliptic curve cryptography has been proposed. The proposed encryption is computationally efficient as it does not need an exclusive encoding of plain image pixels to Elliptic curve points. Results and analysis show that our scheme is resilient to different kinds of security attacks particularly differential attacks. The proposed scheme results in a random cipher image with high entropy and very low inter-pixel correlation at a low computational cost and encryption time.

Image encryption is gaining popularity due to the rapid use of internet technology. Security attacks on the image can occur when images are shared through the internet. E-governance generates important documents with images like identity cards, admit cards, and medical reports. An attacker can steal the image data. An attacker can modify or alter the documents using their own images to create duplicate documents. So, image encryption is a must to prevent the security and privacy of images. In this paper, a novel image encryption method using elliptic curve pseudo-random number is proposed for image data transfer in E-governance. The encryption method creates the cipher image with secp256r1 elliptic curve points and a map table. The map table consists of bit positions as index numbers. In the decryption phase, the receiver decodes the cipher image complementing index numbers in the map table. The map table is also encrypted and decrypted with a chaotic sequence. Our method gives better results than other image encryption methods, according to bits per pixels, entropy, correlation of adjacent pixels, histogram analysis, peak signal to noise ratio, unified average changing intensity, encryption, and decryption time. The experimental results prove the proposed method’s effectiveness and security.

The existing image encryption schemes are not suitable for the secure transmission of large amounts of data in range-gated laser imaging under high noise background. Aiming at this problem, a range-gated laser imaging image compression and encryption method based on bidirectional diffusion is proposed. The image data collected from the range-gated laser imaging source is sparsely represented by the discrete wavelet transform. Arnold chaotic system is used to scramble the sparse matrix, and then the measurement matrix is constructed by the quantum cellular neural network (QCNN) to compress the image. In addition, the random sequence generated by QCNN hyperchaotic system is used to carry out “bidirectional diffusion” operation on the compression result, so as to realize the security encryption of image data. The comparative analysis of the security encryption performance of different compression ratios shows that the histogram sample standard of the encrypted image can reach about 10, and the information entropy value is more than 7.99, which indicates that the encryption scheme effectively hides the plaintext information of the original image. When the encrypted image is attacked by different degrees of noise, this method can still reconstruct the image through the effective decryption process. The experimental results show that this method realizes the secure compression and encryption of gated-laser imaging image data, and effectively ensures the security of data while reducing the amount of channel transmission data.

A novel color image encryption algorithm based on the fractional order laser chaotic system and DNA mutation principle is proposed in this paper. Using phase diagram, Lyapunov exponential spectrum, bifurcation diagram and C0 complexity, the dynamic characteristics of the fractional-order laser chaotic system are analyzed, and the hardware circuit simulation of the chaotic system is realized on the DSP platform. Based on the comprehensive analysis of the system, we have designed an image encryption algorithm. Firstly, the chaotic sequence and Arnold matrix are used to scramble the value of the image. Then, in the diffusion link, DNA diffusion algorithm and DNA mutation theory were used for the first time, which increased the randomness of the matrix. The performances of the designed encryption scheme are analyzed by key space, correlation coefficients, information entropy, histogram, differential attacks and robustness analysis., the experimental results prove that the algorithm has strong encryption capabilities and can withstand multiple decryption methods. The new color image encryption scheme proposed in this paper can realize the secure communication of digital images.

Commutative encryption and watermarking (CEW) can provide comprehensive protection for vector data in geographical information system (GIS) by integrating encryption and watermarking. However, the existing CEW methods for image or video are not suitable for GIS vector data. These CEW methods are still unable to ensure independence between encryption and watermarking for GIS vector data. To solve this problem, this paper proposes a new CEW method based on singular value decomposition (SVD) for GIS vector data. Firstly, the characteristics of SVD are analyzed for GIS vector data. Then, the singular values are selected as the feature invariant. The stability and independence of the singular values are used for embedding watermark and the orthogonal invariance of the singular values is used for encryption. The experimental results show that the proposed method can achieve the independent of encryption and watermarking, as well as strong robustness and high security for commutative encryption and watermarking. The partial decryption can also be realized by the proposed method, which can achieve the network security real-time decryption for GIS vector data.

Color descriptors, which involve the extraction of color information that is robust to illumination variation, are indispensable for accessing reliable visual information as illumination variation is inevitable in many practical cases. There has been many color descriptors proposed in literature, but the performance of different color descriptors in different scenes under illumination variation and the influence of the surface characteristics have not been investigated. In this paper, we first systematically introduced the theoretical basis of color descriptors, categorized the existing color descriptors according to the theoretical basis, and then compared the performance of different color descriptors utilized for image recognition and image retrieval tasks on both the indoor and outdoor image datasets. We adopted the recognition rate and normalized average rank as the evaluation criteria to measure the performance of color descriptors. Experiment results show that the color moment invariants (CMI) provide the optimal balance between the performance and dimensions in most tests, and color descriptors derived from physical reflectance models are more suitable for object recognition and image retrieval. We also concluded the best color descriptors for each kind of scene and surface characteristics.

Encryption and information hiding are widely used in security applications requiring copyright protection, over-communication, and tamper detection. Quantum steganography, using quantum images and audio as information carriers, is a fascinating hybrid of classical and quantum informatics. While existing steganography techniques offer high invisibility, current embedding capacities remain insufficient. As such, a high capacity quantum steganography algorithm, based on image interpolation, is proposed in this study. In this approach, quantum interpolation is conducted by maximizing difference values between neighboring pixels to generate a cover image. Secret message embedding and extraction steps are then developed for the resulting quantum images, in addition to corresponding quantum circuit networks. A series of simulation experiments were conducted to demonstrate the implementation and assess the performance of the proposed technique, the efficiency of which was evaluated using several performance metrics and the visual quality of the stego-images. The resulting information capacity was much higher than that of existing quantum steganography algorithms. This suggests the proposed strategy to be a promising trade-off between invisibility and capacity, providing new motivation to investigate image steganography using quantum computing resources.

We propose an attack on the recent attempt by Li, Xing and Yeo to produce a code-based signature scheme using the Schnorr-Lyubashevsky approach in the Hamming metric, and verify its effectiveness through numerical simulations. Differently from other (unsuccessful) proposals, this new scheme exploits rejection sampling along with dense noise vectors to hide the secret key structure in produced signatures. We show that these measures, besides yielding very slow signing times and rather long signatures, do not succeed in protecting the secret key. We are indeed able to prove the existence of a strong correlation between produced signatures, which ultimately leaks information about the secret key. To support this claim, we use both theoretical arguments and numerical evidences. Finally, we employ such a weakness to mount a full key recovery attack, which is able to recover the secret key after the observation of a bunch of signatures. Our results show that the considered scheme may be secure only for one-time usage.

Physical layer secret key generation is a promising candidate to achieve one-time-pad encryption approach for the wireless communication system. However, in a static environment, the secret key rate is low due to the lack of channel time-variation. To solve this problem, this letter proposes a novel secret key generation scheme assisted by an intelligent reflecting surface with discrete phase shifts. In the scheme, legitimate nodes construct the dynamic time-varying channel by rapidly and randomly switching the phase of IRS elements. Then, channel coefficients are used to generate the secret key. Based on the IRS channel model, we derive the probability distribution of the channel coefficient and the expressions of the secret key rate. Furthermore, we optimize the secret key rate by adjusting the switching time of the IRS phase. Monte Carlo simulation and numerical results show that our proposed scheme can update the secret key in a static environment.

Informal availability of Internet and easy accessibility of network resources to a general public has created a new era of security design challenge for multimedia contents which is available in the form of image, text, video and audio etc. This paper presents a novel technique to employ a security mechanism for transmission of digital contents over public network. The proposed method uses a multilevel image encryption/decryption algorithm based on quantum chaos map and sparse sampling. In the initial phase an original image is divided into blocks of equal size and each block is further divided into sub-block using DWT technique. The pixels of the adjacent sub blocks are exchanged randomly by random matrix. Each frequency band of the blocks is encrypted by modulus function, and then combined each to get the new block. Next, pixels of the neighbor blocks are exchanged randomly by random matrix and subsequently each block is encrypted using modulus function. The result given in graphical and tabular form shows the correctness of the algorithm.

A new image encryption algorithm is proposed by combining fractional Hermite transform (FHT) with hyper-chaos map. In the first round of pixel scrambling, the original image is performed by the Zig...

In real life, images are inevitably interfered by various noises during acquisition and transmission, resulting in a significant reduction in image quality. The process of solving this kind of problem is called image denoising. Image denoising is a basic problem in the field of computer vision and image processing, which is essential for subsequent image processing and applications. It can ensure that people can obtain more effective information of images more accurately. This paper mainly studies a new method of crop image denoising with improved SVD in wavelet domain. The algorithm used in this study firstly carried out a 3-layer wavelet transform on the crop noise image, leaving the low-frequency subimage unchanged; then, for the high-frequency subimages distributed in the horizontal, vertical, and diagonal directions, the improved adaptive SVD algorithm was used to filter the noise; finally perform wavelet coefficient reconstruction. To effectively test the performance of the algorithm, field crop images were taken as test images, and the denoising performance of the algorithm, SVD algorithm, and the improved SVD algorithm used in this study were compared, and the peak signal-to--to-noise ratio (PSNR) was introduced. Quantitative evaluation of the denoising results of several types of algorithms. The experimental data in this paper show that when the noise standard deviation is greater than 20, the enhanced experimental results clearly achieve higher PSNR and SSIM values than WNNM. The average peak signal-to-noise ratio (PSNR) is about 0.1 dB higher, and the average SSIM is larger about 0.01. The results show that the algorithm used in this study is superior to the other two algorithms, which provides a more effective method for crop noise image processing.

This paper proposes an asymmetric encryption scheme for color images by introducing a new efficient triple-layered encryption scheme based on the RSA cryptosystem along with a chaotic map in the di...

Almost all existing image encryption algorithms are only suitable for low-resolution images in the standard image library. When they are used to encrypt high-resolution images, they will inevitably suffer from inefficiency or program crashes. Inspired by the super-pixel concept in image processing, in this paper, a new image encryption algorithm using PRSP (Pseudo Random Super Pixel) strategy and KBCA (Key-based Cellular Automata) is proposed. Both scrambling and diffusion are implemented in parallel to improve the efficiency of encryption algorithm when encrypting ultra-high resolution images. Besides, random diffusion path is designed to ensure the security of encryption algorithm. Simulations are carried out using CUDA (Compute Unified Device Architecture) platform, and simulation results show the high efficiency and security of our algorithm.

This paper introduces a deep learning-based Steganography method for hiding secret information within the cover image. For this, we use a convolutional neural network (CNN) with Deep Supervision based edge detector, which can retain more edge pixels over conventional edge detection algorithms. Initially, the cover image is pre-processed by masking the last 5-bits of each pixel. The said edge detector model is then applied to obtain a gray-scale edge map. To get the prominent edge information, the gray-scale edge map is converted into a binary version using both global and adaptive binarization schemes. The purpose of using different binarization techniques is to prove the less sensitive nature of the edge detection method to the thresholding approaches. Our rule for embedding secret bits within the cover image is as follows: more bits into the edge pixels while fewer bits into the non-edge pixels. Experimental outcomes on various standard images confirm that compared to state-of-the-art methods, the proposed method achieves a higher payload.

This article introduces a robust image watermarking primarily founded on DWT (discrete wavelet transform), BEMD (bi-dimensional empirical mode decomposition), DCT (discrete cosine transform), PSO (particle swarm optimization), and SVD (singular value decomposition). During the process of embedding, 2nd level DWT is used to decompose the cover image into sub-bands. DWT is also used for the decomposition of images for watermarking. Further, BEMD decomposition runs to implement on the selected band of DWT. For optimization, PSO is used for complex and multidimensional searches. Further DCT as well as SVD applied to the selected band. The embedding and scaling factors are embedded with the help of a security key. Further, this method is followed by using the inverse of ISVD, IDCT, IDWT, and IBEMD. The Watermark image is extracted by the extraction process. Experimental results show that the suggested technique is robust compared to several geometrical and non-geometrical attacks. Therefore, this proposed technique provides better visible quality and robustness against numerous attacks like salt and pepper, Gaussian filter, rotation, median filter, speckle, gamma correction, scaling, and shearing for gray scale images and provides the watermarked image with good quality.

In this paper we propose the FD-TR: Feature Detector Based on Scale Invariant Feature Transform and Bidirectional Feature Regionalization for digital image watermarking. The Scale Invariant Feature Transform method is applied to extract keypoints and an Edge and Neighbor Filtering method is proposed to generate the candidate feature points. Then the Bidirectional Feature Regionalization method is proposed and applied in order to classify candidate feature points and form candidate feature regions. On this basis, the Candidate Feature Region Filtering method is proposed to select the final feature regions for watermarking. During the watermarking process, the Nonsubsampled Contourlet Transform is employed to the extracted feature regions to extract the low-frequency coefficients. Next, we use the Diagonal Matrix-based Spread Transform Dither Modulation for watermark embedding and extraction. Extensive experiments have been conducted to evaluate the performance of the proposed scheme and the comparison with existing methods demonstrate that the proposed method is superior to the existing methods in terms of robustness and quality.

Photoelectron ejection is important in technological applications. Recently, the photoelectric effect was explained based on the electromagnetic (EM) wave concept. According to the EM wave concept,...

According to the research works of the references on chaotic cryptanalysis, many recent chaotic image encryption algorithms cannot resist chosen-plaintext attacks. Although some chaotic image encryption algorithms introduce plain-image information, they still violate some design requirements of modern cryptosystems. The traditional image encryption algorithm has the disadvantages of low security and time-consuming. Therefore, after analyzing many literatures related to chaotic image encryption and aiming to improve the security of ciphertext and effectively resist plaintext attack, we propose a hybrid chaotic system-oriented artificial fish swarm neural network method for image encryption. Firstly, based on the nonlinear combination theory, the Logistic, Tent and Sine mappings are used to design the hybrid complex chaotic system. According to the pixel value of plaintext, it generates the initial value to output the chaotic sequence. Taking a chaotic sequence as the input layer, the artificial neural network is introduced to train and learn the chaotic sequence. It eliminates the chaotic periodicity and outputs the neural network sequence. The artificial fish swarm algorithm is used to optimize the initial weight and threshold of the Elman neural network. The set obfuscation method is defined to scramble the plaintext. A quantization method is constructed to quantify the neural network sequence and obtain the key flow. The mixed images are classified, the key flow is combined, the pixel value is changed and the ciphertext is output by designing the piecewise diffusion technology. Experimental results show that compared with the current state-of-the-art image encryption methods, the proposed algorithm has higher security and resist plaintext attack ability.

Color correction and color grading are important steps in film production. Recent palette-based approaches to image recoloring have shown that a small set of representative colors provide an intuitive set of handles for color adjustment. However, a single, static palette cannot represent the time-varying colors in a video. We introduce a spatial-temporal geometry-based approach to video recoloring. Specifically, its core is a 4D skew polytope with a few vertices that approximately encloses the video pixels in color and time, which implicitly defines time-varying palettes through slicing of the 4D skew polytope at specific time values. Our geometric palette is compact, descriptive, and provides a correspondence between colors throughout the video, including topological changes when colors merge or split. Experiments show that our method produces natural, artifact-free recoloring.

The quantum cryptographic system AKM2017 is considered. The results of the analysis of the dependence of the degree of difference between the encryption and decryption gamut on the degree of difference between the corresponding session keys are presented. The equality of these degrees of distinction is revealed and substantiated. For an arbitrarily fixed encryption session key, the distribution of session decryption keys by classes is revealed and described, depending on the value of the degree of difference between the encryption and decryption gamuts. One class is made up of all session decryption keys, leading to the same value of the degree of difference between the encryption and decryption gamuts. A geometric interpretation of the specified distribution by classes is given in the form of placement along circles (class is a circle) on the surface of a sphere of unit radius centered at the origin of the Euclidean rectangular coordinate system in a three-dimensional linear space over the field of real numbers. The stated results can be used to solve the problems of optimizing the values of the parameters of accuracy and reliability of the functioning of variants of practical implementations of the quantum cryptographic system AKM2017, for example, when setting up a session decryption key, which makes it possible to guarantee a predetermined small value of the mathematical expectation of the number of incorrectly decrypted binary plain text.

Although focusing on the field of video generation has made some progress in network performance and computational efficiency, there is still much room for improvement in terms of the predicted frame number and clarity. In this paper, a depth learning model is proposed to predict future video frames. The model can predict video streams with complex pixel distributions of up to 32 frames. Our framework is mainly composed of two modules: a fusion image prediction generator and an image-video translator. The fusion picture prediction generator is realized by a U-Net neural network built by a 3D convolution, and the image-video translator is composed of a conditional generative adversarial network built by a 2D convolution network. In the proposed framework, given a set of fusion images and labels, the image picture prediction generator can learn the pixel distribution of the fitted label pictures from the fusion images. The image-video translator then translates the output of the fused image prediction generator into future video frames. In addition, this paper proposes an accompanying convolution model and corresponding algorithm for improving image sharpness. Our experimental results prove the effectiveness of this framework.

An optimized color image watermarking algorithm with stationary wavelet transform and singular value decomposition has been proposed in this work. The algorithm uses two watermarks to address the issues of copyright protection and tamper localization. A gray-scale image watermark is embedded in the transform domain of the green plane of the cover image to address the issue of copyright protection. A binary logo watermark is inserted in the spatial domain of the blue plane of the cover image by the least significant bit approach. It helps to locate the visible and invisible tampers in the cover image. This work is optimized through the genetic algorithm (GA) to achieve better perceptual quality and robustness. A novel fitness function has been proposed to use with the GA to figure out an optimal scaling factor for the embedding process. The strength of the algorithm has been examined by enforcing several attacks on the watermarked image. The embedding capacity of the algorithm and the time complexity of the proposed fitness function are compared with other existing works.

How to make cloud computing ensure the privacy of data while ensuring its availability in the process of computing data is a big problem faced by cloud computing. Under the background of quantum information processing, a quantum fully homomorphic encryption (QFHE) scheme is proposed. QFHE based on universal quantum circuit (UQC) allows arbitrary quantum transformation. QFHE allows the operation of encrypted data without decryption, which greatly ensures the security of data. In this scheme, the encryption key is constructed by GHZ-like state. Both the encryption key and the decryption key use quantum one-time pad (QOTP), and the decryption key is different from the encryption key. In this scheme, the evaluation algorithm is independent of the encryption key, which is a powerful tool to protect data privacy in the cloud environment. In addition, security analysis and complexity analysis also show that the scheme is suitable for privacy data processing.