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A bus-off attack is a denial-of-service (DoS) attack which exploits error handling in the controller area network (CAN) to induce an honest node to disconnect itself from the CAN bus. This paper develops a stochastic transmission policy as a countermeasure for the controller-transmitter pair against the bus-off attack. We model this as a non-zero-sum linear-quadratic-Gaussian game between the controller-transmitter pair and the attacker. We derive Nash equilibria of the game for two different information structures of the attacker. We show that the attacker has a dominant attack strategy under both information structures. Under the dominant attack strategy, we show that the optimal control policy is linear in the system state. We further identify a necessary and a sufficient conditions on the transmission policy to have bounded average cost. The theoretical results are complemented by a detailed case study of a bus-off attack on a vehicular adaptive cruise control model.

Protecting the parameters, states, and input/output signals of a dynamic controller is essential for securely outsourcing its computation to an untrusted third party. Although a fully homomorphic encryption scheme allows the evaluation of controller operations with encrypted data, an encrypted dynamic controller with the encryption scheme destabilizes a closed-loop system or degrades the control performance due to overflow. This paper presents a novel controller representation based on input-output history data to implement an encrypted dynamic controller that operates without destabilization and performance degradation. Implementation of this encrypted dynamic controller representation can be optimized via batching techniques to reduce the time and space complexities. Furthermore, this study analyzes the stability and performance degradation of a closed-loop system caused by the effects of controller encryption. A numerical simulation demonstrates the feasibility of the proposed encrypted control scheme, which inherits the control performance of the original controller at a sufficient level., Comment: 12 pages, 7 figures

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.

This paper is concerned with the security issue in the state estimation problem for a networked control system (NCS). A new model of joint false data injection (FDI) attack is established wherein attacks are injected to both the remote estimator and the communication channels. Such a model is general that includes most existing FDI attack models as special cases. The joint FDI attacks are subjected to limited access and/or resource constraints, and this gives rise to a few attack scenarios to be examined one by one. Our objective is to establish the so-called insecurity conditions under which there exists an attack sequence capable of driving the estimation bias to infinity while bypassing the anomaly detector. By resorting to the generalized inverse theory, necessary and sufficient conditions are derived for the insecurity under different attack scenarios. Subsequently, easy-to-implement algorithms are proposed to generate attack sequences on insecure NCSs with respect to different attack scenarios. In particular, by using a matrix splitting technique, the constraint-induced sparsity of the attack vectors is dedicatedly investigated. Several numerical examples are

With the wide application of cyber-physical systems, stealthy attacks on remote state estimation have attracted increasing research attention. Recently, various stealthy innovation-based linear attack models were proposed, in which the relaxed stealthiness was measured by the Kullback--Leibler divergence. This paper studies existing innovation-based linear attack strategies with relaxed stealthiness and concludes that all of them provided merely suboptimal solutions. The main reason is some oversight in solving the involved optimization problems: some covariance constraints were not perfectly handled. This paper provides the corresponding optimal solutions for those stealthy attacks. Both one-step and holistic optimizations of stealthy attacks are studied, and the worst-case attacks with and without zero-mean constraints are derived analytically, without the necessity to formulate semidefinite programming problems.

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.

Sparse stealth attack constructions that minimize the mutual information between the state variables and the observations are proposed. The attack construction is formulated as the design of a multivariate Gaussian distribution that aims to minimize the mutual information while limiting the Kullback-Leibler divergence between the distribution of the observations under attack and the distribution of the observations without attack. The sparsity constraint is incorporated as a support constraint of the attack distribution. Two heuristic greedy algorithms for the attack construction are proposed. The first algorithm assumes that the attack vector consists of independent entries, and therefore, requires no communication between different attacked locations. The second algorithm considers correlation between the attack vector entries which results in better attack performance at the expense of coordination between different locations. We numerically evaluate the performance of the proposed attack constructions on IEEE test systems and show that it is feasible to construct stealth attacks that generate significant disruption with a low number of compromised sensors., Comment: 10 pages, 6 figures, submited to IEEE Trans. Smart Grid

A novel deterministic sensing matrix design approach applied to enable secured compressed sensing and transmission of fingerprint images. The performance of the sensing matrix was analyzed in detail by varying compressed sensing and security parameters. The number of sampling and sparse coefficient are the parameters taken under consideration from compressed sensing, whereas the encryption key is from the security scheme. The first stage in the performance study is acquiring the signal, and followed by sparsely modelling it using Orthogonal Matching Pursuit (OMP) algorithm. The sparse modelled data is multiplied by the proposed deterministic partial orthogonal Discrete Cosine Transform (DCT) sensing matrix to reduce its dimension and encrypt it. To introduce confusion on the DCT matrix rows, the pseudo-random permutation is applied to the DCT matrix rows before sensing matrix derivation. Additionally, recovering and decryption of the compressed image accomplished with the help of a convex optimization method. The results obtained from the simulation of the proposed system confirmed that a better Peak Signal to Noise Ratio (PSNR) than the recommended value for wireless transmission is archived using a sample below 25% without losing a significant number of fingerprint minutiae.

Image watermarking commonly involves singular value decomposition (SVD) because of its simplicity and minimal effect on image quality. However, SVD-based image watermarking schemes suffer from some drawbacks such as the false positive problem (FPP), and an undesirable trade-off between vital properties such as imperceptibility, embedding capacity, and robustness. To address these drawbacks, we improve upon the SVD-based color image watermarking by incorporating integer wavelet transform (IWT) and chaotic maps. A grayscale image watermark is decomposed into eight gray levels (bit-planes) before being encrypted and re-ordered by a chaotic sequence. Each encrypted bit-plane is inserted into singular values of the sub-bands of a host image. The embedding process is controlled by chaos-based multiple scaling factors (MSF) which are updated in each embedded bit-plane. The resulting values are involved in generating a hash value at the end of the embedding processes. The hash value is used to overcome FPP issues and improves security. Our findings illustrate the proposed scheme’s robustness, security, imperceptibility, and capacity. It also exhibits excellent robustness against attacks and its performance surpasses a variety of existing schemes. In addition, the proposed scheme is hypersensitive to even the slightest secret key variations.

We present a method to search for a probe (or query) image representation against a large gallery in the encrypted domain. We require that the probe and gallery images be represented in terms of a fixed-length representation, which is typical for representations obtained from learned networks. Our encryption scheme is agnostic to how the fixed-length representation is obtained and can therefore be applied to any fixed-length representation in any application domain. Our method, dubbed HERS (Homomorphically Encrypted Representation Search), operates by (i) compressing the representation towards its estimated intrinsic dimensionality with minimal loss of accuracy (ii) encrypting the compressed representation using the proposed fully homomorphic encryption scheme, and (iii) efficiently searching against a gallery of encrypted representations directly in the encrypted domain, without decrypting them. Numerical results on large galleries of face, fingerprint, and object datasets such as ImageNet show that, for the first time, accurate and fast image search within the encrypted domain is feasible at scale (500 seconds; $275\times$ speed up over state-of-the-art for encrypted search against a gallery of 100 million). Code is available at https://github.com/human-analysis/hers-encrypted-image-search, Comment: Published in the Trustworthy Biometrics Special Issue of IEEE Transactions on Biometrics, Behavior, and Identity Science 2021

In this letter, we analyze the performance of covert communications under faster-than-Nyquist (FTN) signaling in the Rayleigh block fading channel. Both Bayesian criterion- and Kullback-Leibler (KL) divergence-based covertness constraints are considered. Especially, for KL divergence-based one, we prove that both the maximum transmit power and covert rate under FTN signaling are higher than those under Nyquist signaling. Numerical results coincide with our analysis and validate the advantages of FTN signaling to realize covert data transmission., Comment: We have corrected the typos and inappropriate description throughout the paper as reviewers suggested. We have proved the superiority of FTN signaling on covert communications with the same detection time duration at Willie in Theorem 3 and renewed the simulation results in Section V as Reviewer 2 suggested. This paper has been resubmitted to IEEE Communications Letters on 14-Jan-2022

EM enhancement is typically a lensing-type of effect by which a wide input beam couples to spatially localized resonant modes as for example Mie plasmons, void plasmons or Mie modes at high refractive index particles. Accordingly, EM enhancement is inherently a subwavelength phenomenon. Here, we explore a different paradigm for EM enhancement that is based on EM energy being collected over large time intervals by means of an ultra-slow-light waveguide. With ab-initio simulations, we study the dynamic evolution of such EM enhancement. After more than 7000 wave periods we observe that an intensity enhancement of more than 50 can cover an area of more than 15 $\lambda_{free^{2}}$ with $\lambda_{free}$ being the free-space wavelength.

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)

We present the results of fitting a flexible stellar stream density model to a collection of thirteen streams around the Milky Way, using photometric data from DES, DECaLS, and Pan-STARRS. We construct density maps for each stream and characterise their tracks on the sky, width, and distance modulus curves along the length of each stream. We use these measurements to compute lengths and total luminosities of streams and identify substructures. Several streams show prominent substructures, such as stream broadening, gaps, large deviations of stream tracks and sharp changes in stream densities. Examining the group of streams as a population, as expected we find that streams with globular cluster progenitors are typically narrower than those with dwarf galaxy progenitors, with streams around 100 pc wide showing overlap between the two populations. We also note the average luminosity of globular cluster streams is significantly lower than the typical luminosity of intact globular clusters. The likely explanation is that observed globular cluster streams preferentially come from lower luminosity and lower density clusters. The stream measurements done in a uniform manner presented here will be helpful for more detailed stream studies such as identifying candidate stream members for spectroscopic follow up and stellar stream dynamical modeling., Comment: 25 pages, 40 figures, 4 tables. Accepted for publication in MNRAS

This article proposes an image encryption algorithm based on a chaotic bit-plane decomposition and optimization algorithm of a crossover operator artificial bee colony algorithm. Firstly, use the SHA-256 hash algorithm to calculate the plaintext image’s hash value as the starting value of the fractional Lorenz hyperchaotic system after operation. Utilize the chaotic sequence to permutate plaintext image in a bit plane to obtain the scrambled image. Secondly, block the scrambled image into four subimages of equal size, and count the hash value of each row of each block by the SHA-256 hash algorithm as the starting value of the Sine-Tent-Logistic chaotic system. Use the obtained chaotic sequence to substitute the images. Then, stitch the four sub-block images to get the final encrypted image, and the population is obtained. Finally, use the information entropy of ciphertext image as the fitness function of the artificial bee colony algorithm based on a crossover operator. Select the ciphertext image with the best information entropy from the population as the optimal encrypted image, and then, return the position value of the best honey source meanwhile. The experimental simulation and security analysis indicate that the scheme has an excellent encryption effect and ability to oppose various general attacks.

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.

Detecting double JPEG compression with the same quantization matrix is a challenging task in image forensics. To address this problem, in this paper, a novel method is proposed by leveraging the component convergence during repeated JPEG compressions. Firstly, an in-depth analysis of the pipeline in successive JPEG compressions is conducted, and it reveals that the rounding/truncation errors as well as JPEG coefficients tend to converge after multiple recompressions. Based on this fact, the backward quantization error (BQE) is defined, and we find that the ratio of non-zero BQE for single compression is larger than that for double compression. Moreover, to exploit the convergence property of JPEG coefficients, a multi-threshold strategy is designed for capturing the statistics of the number of different JPEG coefficients between two sequential compressions. Finally, the statistical features of the dual components are concatenated into a 15-D vector to detect double JPEG compression. Experimental results demonstrate the efficiency of the proposed method, which outperforms some state-of-the-art schemes.

This paper focuses on investigating the tradeoff problem between the attack stealthiness and the attack performance in cyber-physical systems (CPSs). The notion of (,)-stealthiness is utilized to characterize the stealthiness level and the corrupted error covariance of the remote estimator is adopted to measure the attack performance. The aim is to design an off-line stealthy attack strategy to maximize the attack performance. Different from the existing results, where the relationship between attack performance and system stability is not provided, we prove that the corrupted error covariance has an upper bound for stable systems and can be arbitrarily large for unstable systems. To maximize the attack effect under a given (,)-stealthiness constraint, a non-convex optimization problem is formulated. By applying convex analysis and monotonic optimization techniques, an upper bound of the attack performance is presented for stable systems. Subsequently, a (,)-stealthy attack signal is constructed to achieve the upper bound. Finally, simulations on a stable numerical example and experiments on the permanent magnet synchronous machine (PMSM) monitoring system are performed to demonstrate the theoretical results.

In this article, an intelligent attacker is considered, which aims to prevent leader-following multiagent systems from achieving consensus. The attacker randomly injects denial-of-service (DoS) attacks to some communication channels in the network, which make the corresponding attacked edges disconnected. The minimum number of communication channels needed to be jammed by the attacker to guarantee the system fails to achieve the consensus is provided based on the Max-Flow Min-Cut lemma and an algorithm to generate the minimum attacked edges is proposed. Furthermore, a lower bound and an upper bound of the attack probability to destroy the consensusability of the system are provided, respectively. Finally, numerical simulations are given to illustrate the results.

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.

This paper considers the worst-case stealthy attack strategies on stochastic event-based state estimation. Smart sensors equipped with local event-triggered Kalman filters are used to transmit innovations. Contrary to classic Kalman filters, the transmitted innovation screened by the stochastic decision rule does not follow a Gaussian distribution. A special type of distribution called complete Gaussian crater is defined and analyzed, which is essential for designing stealthy attacks. The evolution of the estimation error covariance under attacks is obtained. Stealthy attacks that yield the greatest estimation errors under constraints on transmissions and distributions are obtained and analyzed. The system performance degradation caused by different attacks is evaluated via simulations.

In order to protect network information security and improve the security of wireless sensor networks, based on chaotic systems, we propose a wireless sensor algorithm based on improved identity encryption. First, the basic principle of chaotic system mapping is specifically analyzed; the two chaotic mapping systems are rectified to obtain the hybrid chaotic mapping system according to the demand of wireless sensing network. After that, an encryption framework and key are designed and the hybrid mapping system is applied to the encryption framework to encrypt the data. In this way, the length of the encryption algorithm is lengthened and the defensibility of the encrypted content is improved. Finally, the performance of the proposed encryption algorithm is tested in terms of information entropy, statistical methods, and ciphertext randomness. The test results show that compared with other encryption algorithms, the running speed of the encryption algorithm proposed in this paper is only 18.51 ms, which is faster than that of other encryption algorithms, and the memory consumption is only 27%, which is much lower than that of other algorithms. It can be seen that the proposed encryption algorithm has strong encryption effect and superior algorithm performance for data encryption in wireless sensor networks.

In the mobile edge computing environments, Quality of Service (QoS) prediction plays a crucial role in web service recommendation. Because of distinct features of mobile edge computing, i.e., the mobility of users and incomplete historical QoS data, traditional QoS prediction approaches may obtain less accurate results in the mobile edge computing environments. In this paper, we treat the historical QoS values at different time slots as a temporal sequence of QoS matrices. By incorporating the compressed matrices extracted from QoS matrices through truncated Singular Value Decomposition (SVD) with the classical ARIMA model, we extend the ARIMA model to predict multiple QoS values simultaneously and efficiently. Experimental results show that our proposed approach outperforms the other state-of-the-art approaches in accuracy and efficiency.

Low surface brightness (LSB) galaxies are galaxies with central surface brightness fainter than the night sky. Due to the faint nature of LSB galaxies and the comparable sky background, it is difficult to search LSB galaxies automatically and efficiently from large sky survey. In this study, we established the low surface brightness galaxies autodetect (LSBG-AD) model, which is a data-driven model for end-to-end detection of LSB galaxies from Sloan Digital Sky Survey (SDSS) images. Object-detection techniques based on deep learning are applied to the SDSS field images to identify LSB galaxies and estimate their coordinates at the same time. Applying LSBG-AD to 1120 SDSS images, we detected 1197 LSB galaxy candidates, of which 1081 samples are already known and 116 samples are newly found candidates. The B-band central surface brightness of the candidates searched by the model ranges from 22 to 24 mag arcsec−2, quite consistent with the surface brightness distribution of the standard sample. A total of 96.46 per cent of LSB galaxy candidates have an axial ratio (b/a) greater than 0.3, and 92.04 per cent of them have $fracDev\_r$ < 0.4, which is also consistent with the standard sample. The results show that the LSBG-AD model learns the features of LSB galaxies of the training samples well, and can be used to search LSB galaxies without using photometric parameters. Next, this method will be used to develop efficient algorithms to detect LSB galaxies from massive images of the next-generation observatories.

With the rapid spread of network information, the information maintenance has become the focus of information security on networks. Digital watermarking is one of the effective methods to protect information security, achieve anticounterfeiting traceability, and protect copyright, and it is an important branch of information hiding technology. However, one of the most challenging questions of digital watermarking is how to present strong robustness in geometric attacks. Nowadays, most watermarking algorithms are relatively weak robustness against geometric attacks. A robust watermarking algorithm against geometric attacks based on the non-subsampled shearlet transform and the Harris-Laplace detector is proposed. The host image is decomposed into subbands with different directions by the shearlet transform, and the Harris-Laplace detector is utilized to obtain the feature regions. Then, the nonoverlapping regions with strong robustness are selected to embed watermark by the fuzzy c-means cluster algorithm. The experimental results indicate that the proposed watermarking scheme can well resist geometric attacks.

Robust watermarking is an effective method and a promising solution for securing and protecting the copyright of digital images. Moments and moment invariants have become popular tools for robust watermarking due to their geometric invariance and favorable capability of image description. Many moments-based robust watermarking schemes have been proposed. However, there is a challenging problem of these schemes that should be addressed. One of these problems is to improve both imperceptibility and robustness. In contrast, the other problem, most of these schemes used inefficient, traditional computation methods of the moments, resulting in an inaccurate and inefficient performance of the watermarking schemes. To overcome these challenges, in this paper, we propose a novel robust color image-watermarking algorithm based on new multiple fractional multi-channel orthogonal moments, fractional-order exponent moments (MFrEMs), fractional-order polar harmonic transforms (MFrPHTs), and fractional-order radial harmonic Fourier moments (MFrRHFMs). Firstly, highly accurate fractional new multi-channel orthogonal moments are computed for the host color images. Then, more stable and accurate coefficients of fractional new multi-channel orthogonal moments are selected. Finally, a robust color image watermarking approach for multiple watermarks images is proposed based on MFrEMs, MFrPHTs, and MFrRHFMs using a 1D Sine chaotic map. The experimental results demonstrate that the proposed approach provides robustness against various attacks and better imperceptibility than the existing methods.

Steganography, as it is known, is a technique to hide a secret message within a message or collection of data that is not secret, and a problem in mathematics is to decipher the secret included in the message, to solve this problem a good tool It is the theory of codes. Unlike the existing works that use block codes to hide information using the steganographic process, in this work, we propose the use of convolutional coding theory in steganography to encrypt and decrypt messages methods to decrypt messages. Here, we suggest a steganographic protocol based on convolutional codes in which they are defined as discrete linear dynamical systems with which the properties on controllability and observability characteristic of linear systems theory can be applied, in particular the properties of output observability character which can be easily described using matrix language. The proposed decoding algorithm used for dissimulation is a linear decoding method, which has decreased both the time and space complexity, compared to the Viterbi decoding algorithm, sometimes used in other cases; indeed, we go from 2h.n to 2h=2.n, in memory space (with h: constraint height, and n: length of cover object). Moreover, the time complexity is better, while we can also denote that with the convolutional approach, we intend to take advantage of the time-depending transaction.

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.

Stego-images are often contaminated by interchannel noise or active noise attack when communicating on the Web. And it is challenging to restore embedded image from corrupted stego-image. This paper studies a kNN-bit approximation algorithm to remove noises in embedded image. The proposed algorithm distinguishes reliable bits from extracted bits, and estimates pixel values by keeping reliable bits unchanged and correcting unreliable bits. Specifically, the 8th (highest) unreliable bit of a pixel can be approximated with its nearest neighbor pixels. And then, if an unreliable bit locates at any one of the 5th-7th bits of a pixel, it is adjusted with two nearest neighbors of the pixel, where the pixel is in-between these two nearest neighbors. Finally, for other unreliable bits, each one is approximated by the maximum and minimum possible values of nearest neighbors of its pixel. We conduct experiments for illustrating the efficiency, and demonstrate that the proposed algorithm can recover the embedded images with good visual quality from corrupted stego-images.

Image watermarking technique aims at high imperceptibility of the embedded watermark with minimal distortion in the watermarked images. In frequency-domain, block-based Discrete Cosine Transform (DCT) is a popular method which can be improved by different scaling factors. This paper presents an adaptive scaling factor for selected DCT coefficients in image watermarking. The image blocks that have lowest pixel variances are chosen as the embedding locations. The optimal scaling factors for selected DCT coefficients on the middle frequencies are obtained by finding the best quality of images. The embedding process is performed using the obtained scaling factors. The proposed technique was investigated to verify the robustness and imperceptibility against various image-processing attacks. It is proved that our technique achieves higher robustness against noise addition, filter and compression than the existing schemes in most cases. Our technique produces greater imperceptibility than the existing schemes.

For a secure multimedia data exchange, watermarking is used. Generally, meta-heuristic optimization is required to optimize scheme parameters in the watermarking embedding. Even though meta-heuristic methods have been widely used because of their enhancing capability, due to their large time consumption nature they are not applicable for time-sensitive applications. This paper proposes a time efficient optimization method based on machine learning algorithms to detect the best embedding parameter for image watermarking with both robustness and imperceptibility. Initially, for providing robustness against watermarking attacks, a method for watermarking embedding is designed in the domain of Discrete Cosine Transform. After that, Ant Colony optimization is used for finding the optimization parameters. Then an observation data is obtained, which includes the feature vector and optimum strength values of the training images. The image features are extracted at different embedded strength values by the calculation of optimum fitness function. Finally Light Gradient Boosting algorithm (LGBA) is applied to predict the optimum embedding parameters of the set of new images which are to be watermarked. When compared with the existing optimization methods, it has been found that the proposed method consumes very less time for the evaluation of optimum solutions. From the results, it has been identified that the proposed algorithm satisfies the image watermarking with the improvement in time enhancement. The effectiveness of the proposed method is analyzed using MATLAB 2018b.

Image encryption is highly required in the big data healthcare cloud to improve the security of the medical image for remote access. Data hiding method is the process of storing the medical information of the patient in the medical image in the hidden format. Many existing data hiding methods are based on wavelet and chaotic map due to its effectiveness. Wavelet based methods have limitations of lack of phase information, poor directionality, and shift sensitivity. Chaotic map is applied to improve the security of the medical image and chaotic map has the limitation of low sensitive to control parameters and initial conditions. In this research, the Improved Chaos Encryption (ICE) is applied to improve the security based on randomness. The average energy is calculated in the images and compared with adaptive threshold to segment the Lorenz 96 model applied in the chaos encryption algorithm to improve the model security. Lorenz 96 increased the randomness of the chaos encryption method due to its high sensitivity. Medial images were used to test the performance of the ICE in the image encryption and image hiding. The proposed ICE model evaluated the quality of the recovered and decrypted image in the various embedding rate. The result shows that the proposed ICE model has the PSNR value of 104.7 dB compared to the LSB-ROI method which has 97.61 dB PSNR.

Semantic segmentation is effective in numerous object classification tasks such as autonomous vehicles and scene understanding. With the advent in the deep learning domain, lots of efforts are seen in applying deep learning algorithms for semantic segmentation. Most of the algorithms gain the required accuracy while compromising on their storage and computational requirements. The work showcases the implementation of Convolutional Neural Network (CNN) using Discrete Cosine Transform (DCT), where DCT exhibit exceptional energy compaction properties. The proposed Adaptive Weight Wiener Filter (AWWF) rearranges the DCT coefficients by truncating the high frequency coefficients. AWWF-DCT model reinstate the convolutional layers giving modularity in the design using multi scale convolution block. The impact of selection of DCT coefficients in the proposed model is validated on the benchmark database as City Spaces. The same level of accuracy compared to the conventional algorithm is achieved using only 40 % of the DCT coefficients. Extensive experiments validate the advantages of adaptive DCT modeling of CNN in semantic segmentation and image classification.

By introducing the BHT algorithm into the slide attack on 1K-AES and the related-key attack on PRINCE, we present the corresponding quantum attacks in this paper. In the proposed quantum attacks, we generalize the BHT algorithm to the situation where the number of marked items is unknown ahead of time. Moreover, we give an implementation scheme of classifier oracle based on Quantum Phase Estimation algorithm in presented quantum attacks. The complexity analysis shows that the query complexity, time complexity and memory complexity of the presented quantum attacks are all $\mathcal{O}(2^{n/3})$ when the success probability is about $63\%$, where $n$ is the block size. Compared with the corresponding classical attacks, the proposed quantum attacks can achieve subquadratic speed-up under the same success probability no matter on query complexity, time complexity or memory complexity. Furthermore, the query complexity of the proposed quantum slide attack on 1K-AES is less than Grover search on 1K-AES by a factor of $2^{n/6}.$ When compared with the Grover search on PRINCE, the query complexity of the presented quantum attack on PRINCE is reduced from $\mathcal{O}(2^{n})$ to $\mathcal{O}(2^{n/2}).$ When compared with the combination of Grover and Simon’s algorithms on PRINCE, the query complexity of our quantum attack on PRINCE is reduced from $\mathcal{O}(n\cdot 2^{n/2})$ to $\mathcal{O}(2^{n/2}).$ Besides, the proposed quantum slide attack on 1K-AES indicates that the quantum slide attack could also be applied on Substitution-Permutation Network construction, apart from the iterated Even-Mansour cipher and Feistel constructions.

In recent years, encryption technology has been developed rapidly and many image encryption methods have been put forward. The chaos-based image encryption technique is a modern encryption system for images. To encrypt images, it uses random sequence chaos, which is an efficient way to solve the intractable problem of simple and highly protected image encryption. There are, however, some shortcomings in the technique of chaos-based image encryption, such limited accuracy issue. The approach focused on the chaotic system in this paper is to construct a dynamic IP permutation and S-Box substitution by following steps. First of all, use of a new IP table for more diffusion of all image pixels based on a 1D logistic map to build IP table. Secondly, a new S-Box based on 2D-Henon chaos was created using more confusion to replace G-channel image data. Finally, design of a modern image encryption approach. This approach uses the key process confusion and diffusion operation and depend on IP and S-Box proposals in the encryption process and several shuffling operations using the 3D- Lornez chaos theory. Theoretical research and simulation suggest that starting sensitivity value of this method is high, has high protection, and encryption speed. Moreover, it also holds the value of the neighboring RGB close to zero. The studies show that the information security capabilities would be both safer and more efficient, as a result of our image quality assessment study. Number of Differential Pixel Rate Change Attacks (NPSR), Unified Average Altered Intensity (UACI), are quality and strength of encryption processing are proved by pixel correlation, Entropy to be good results.

The colorization of the gray-scale image is the process of using color image (has a similar "mood") to add color to a grayscale image. In this paper, we proposed new approach for colorization gray-scale images depending on Singular Value Decomposition (SVD). We used SVD to add color to a grayscale image by determining the best pixel value in the reference image, based on comparing the first column values from the left singular vector matrix of 3x3 block and the pixel value in the center of block of the gray image with other corresponding values in the reference image. Finally, use the best match to transfer color from color image to gray-scale image. The results of the proposed colorization approach are good and plausible.

The steganography is applied on text, image, video, and audio files. The steganography is useful for safe and secure data transmission. Video steganography is used to preserve confidential information of security applications. To improve security of the message, pixels locations are optimized using nature inspired algorithm. As conventional algorithms have a low convergence rate a new algorithm is proposed. A New algorithm is developed by combining two model algorithms namely, Water wave optimization (WWO) and Earth worm optimization (EWO) and is renamed as proposed Water-Earth Worm Optimization (WEWO) algorithm. The frames are preprocessed and extracted using Discrete Cosine transform (DCT) and Structured Similarity index (SSIM), respectively, as regular processing. For pixel prediction, the fitness function is obtained from neighborhood entropies in proposed algorithm. In this method, secret message is embedded with two level decomposition of Wavelet Transform (WT). In the proposed work is tested with ‘CAVIAR’ dataset. The Proposed WEWO-Deep RNN algorithm performance is tested with modular noises such as, pepper, salt and pepper noises. The proposed method gives enhanced performance, which is seen with the parameters, Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and Correlation Coefficient (CC) which defines image quality indices.

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.

A modified multiway pixel-value differencing method for image steganography using general quantization ranges of pixel pairs’ difference values is proposed, where the widths of the quantization ranges are not limited to be powers of two. The method can be employed to embed a secret message into a cover image with a higher embedding rate. The cover image is partitioned into non-overlapping complete or incomplete blocks using $1 \times 3$ , $2 \times 2$ , $2 \times 3$ , or $3 \times 3$ block templates. The message bitstream, whose bit positions are randomized in advance by use of a random number generator, can be embedded in the resulting blocks. More specifically, one pixel in each block is set as a shared pixel, and the least-significant-bit substitution method with the optimal pixel adjustment process is employed to embed message bits into the shared pixel. Next, the shared pixel in the block is combined with each remaining pixel to form a pixel pair, which is then utilized to embed message bits by a new pixel-value differencing method. Also, the use of non-power-of-two range widths is accomplished by a multiple-based number conversion mechanism, and the shared pixel in each block, as well as each related pixel pair whose two grayscale values pass a falling-off-boundary check, are used to embed the digits of the resulting multiple-based number. The experimental results show that the embedding rates yielded by the proposed method are higher than those yielded by existing multiway pixel-value differencing methods. The stego-images resulting from embedding secret messages retain good image quality, and the RS steganalysis process cannot detect the presence of the embedded secret messages.

Security of personal information has become a major concern due to the increasing use of the Internet by individuals in the digital world. The main purpose here is to prevent an unauthorized person from gaining access to confidential information. The solution to such a problem is by authentication of users. Authentication has a very important role in achieving security. Mutually orthogonal graph squares (MOGS) are considered the generalization of mutually orthogonal Latin squares (MOLS). Also, MOGS are generated from edge decompositions of complete bipartite graphs by isomorphic graphs. Graph-orthogonal arrays can be constructed by MOGS. In this paper, graph-orthogonal arrays are used for constructing authentication codes. These arrays are the encoding matrices of authentication tags. We introduce the concepts and basic theorems of MOGS, graph-orthogonal arrays, and authentication codes. After constructing graph-orthogonal arrays by MOGS, then there is an established mapping between graph-orthogonal arrays and message set. This manages us to construct perfect non-splitting and splitting Cartesian authentication codes. In both cases, we calculate the probabilities of successful impersonation attacks and substitution attacks. Besides that, the performance of constructed non-splitting and splitting authentication codes is analyzed. In the end, optimal authentication codes and secure authentication codes are constructed.

In this paper we present a novel switching function for multiplicative watermarking systems. The switching function is based on the algebraic structure of elliptic curves over finite fields. The resulting function allows for both watermarking generator and remover to define appropriate system parameters, sharing only limited information, namely a private key. Given the definition of the switching function, we prove that the resulting watermarking parameters lead to a stable watermarking scheme., Comment: 8 pages, 3 figures, to be presented at: 11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes - SAFEPROCESS

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

Color filter array is a spatial multiplexing of pixel-sized filters fabricated over pixel sensors in most color image sensors. The state-of-the-art lossless coding techniques of raw sensor data captured by such sensors leverage spatial or cross-color correlation using lifting schemes. In this paper, we propose a lifting-based lossless white balance algorithm. When applied to the raw sensor data, the spatial bandwidth of the implied chrominance signals decreases. We propose to use this white balance as a pre-processing step to lossless CFA subsampled image/video compression, improving the overall coding efficiency of the raw sensor data.

We propose seismic elastic full-waveform inversion (EFWI) using both the multiparametric approximate Hessian and the discrete cosine transform (DCT). EFWI is a promising technology for obtaining physical parameters. EFWI using the multiparametric approximate Hessian can suppress crosstalk artifacts for each physical parameter, but the computational burden increases as the number of physical parameters considered increases. In this study, to reduce the computational burden, DCT was used to compress the model parameters and accelerate EFWI in the truncated DCT domain. EFWI using DCT is possible to increase the calculation efficiency by reducing the total number of unknown variables. We implemented EFWI using monoparametric Hessian and multiparametric Hessian, which reduced the computational burden using DCT and conducted tests through numerical experiments using each Hessian. In numerical result, despite using a small number of DCT coefficients, we confirmed that contamination due to crosstalk artifacts was suppressed when EFWI was performed using the multiparametric approximate Hessian.

Discrete transforms are used in many signal processing applications such as audio compression, image compression, video compression, high efficiency video coding (HEVC), and so on. This paper proposes a generic VLSI architecture for performing ( N × N × N )-point discrete transformation. This proposed generic architecture can be used to perform the 3D discrete transformations such as Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), Discrete Hartley Transform, Integer Discrete Cosine Transform (Integer DCT), Discrete Hadamard Transform, and Discrete Walsh Transform. Also, this ( N × N × N )-point discrete transformation’s proposed architecture is to perform ⌊ N 2 i ⌋ numbers of ( 2 i × 2 i × 2 i )-point discrete transforms in parallel, where i is varied from 2, 3, 4, … l o g 2 N for Discrete Hartley Transform, i = 3 for DCT/DST, and i = 2 , 3 , 4 , 5 , 6 for Integer DCT/Discrete Walsh/Discrete Hadamard Transforms. Also, our proposed architecture eliminates the requirement of larger storage buffer in between the row, column, and temporal processes. The trade-off in our proposed design is the number of cycles to complete the operation. All the existing and proposed techniques are implemented with 45 nm CMOS technology using Cadence. The synthesis results show that our proposed architecture achieves 73% of reduction in critical path delay as compared with the parallel Butterfly architecture based 3D-DCT.

This paper constructs two encryption methods using 2-D chaotic maps, Duffings and Arnold’s cat maps respectively. Both of the methods are designed using message embedded scheme and are analyzed for their validity, for plaintext sensitivity, key sensitivity, known plaintext and brute-force attacks. Due to the less key space generally many chaotic cryptosystem developed are found to be weak against Brute force attack which is an essential issue to be solved. For this issue, concept of identifiability proved to be a necessary condition to be fulfilled by the designed chaotic cipher to resist brute force attack, which is a basic attack. As 2-D chaotic maps provide more key space than 1-D maps thus they are considered to be more suitable. This work is accompanied with analysis results obtained from these developed cipher. Moreover, identifiable keys are searched for different input texts at various key values. The methods are found to have good key sensitivity and possess identifiable keys thus concluding that they can resist linear attacks and brute-force attacks.

With the development of image recovery models,especially those based on adversarial and perceptual losses,the detailed texture portions of images are being recovered more naturally.However,these restored images are similar but not identical in detail texture to their reference images.With traditional image quality assessment methods,results with better subjective perceived quality often score lower in objective scoring.Assessment methods suffer from subjective and objective inconsistencies.This paper proposes a regional differential information entropy (RDIE) method for image quality assessment to address this problem.This approach allows better assessment of similar but not identical textural details and achieves good agreement with perceived quality.Neural networks are used to reshape the process of calculating information entropy,improving the speed and efficiency of the operation. Experiments conducted with this study image quality assessment dataset and the PIPAL dataset show that the proposed RDIE method yields a high degree of agreement with people average opinion scores compared to other image quality assessment metrics,proving that RDIE can better quantify the perceived quality of images., Comment: 14 pages, 8 figures, 5 tables

In this paper, we propose a novel conservative chaotic standard map-driven dynamic DNA coding (encoding, addition, subtraction and decoding) for the image encryption. The proposed image encryption algorithm is a dynamic DNA coding algorithm i.e., for the encryption of each pixel different rules for encoding, addition/subtraction, decoding etc. are randomly selected based on the pseudorandom sequences generated with the help of the conservative chaotic standard map. We propose a novel way to generate pseudo-random sequences through the conservative chaotic standard map and also test them rigorously through the most stringent test suite of pseudo-randomness, the NIST test suite, before using them in the proposed image encryption algorithm. Our image encryption algorithm incorporates a unique feed-forward and feedback mechanisms to generate and modify the dynamic one-time pixels that are further used for the encryption of each pixel of the plain image, therefore, bringing in the desired sensitivity on plaintext as well as ciphertext. All the controlling pseudorandom sequences used in the algorithm are generated for a different value of the parameter (part of the secret key) with inter-dependency through the iterates of the chaotic map (in the generation process) and therefore possess extreme key sensitivity too. The performance and security analysis has been executed extensively through histogram analysis, correlation analysis, information entropy analysis, DNA sequence-based analysis, perceptual quality analysis, key sensitivity analysis, plaintext sensitivity analysis, etc., The results are promising and prove the robustness of the algorithm against various common cryptanalytic attacks., Comment: 29 pages, 5 figures, 15 tables

In this paper, we propose a multiresidual module convolutional neural network-based method for athlete pose estimation in sports game videos. The network firstly designs an improved residual module based on the traditional residual module. Firstly, a large perceptual field residual module is designed to learn the correlation between the athlete components in the sports game video within a large perceptual field. A multiscale residual module is designed in the paper to better solve the inaccuracy of the pose estimation due to the problem of scale change of the athlete components in the sports game video. Secondly, these three residual modules are used as the building blocks of the convolutional neural network. When the resolution is high, the large perceptual field residual module and the multiscale residual module are used to capture information in a larger range as well as at each scale, and when the resolution is low, only the improved residual module is used. Finally, four multiresidual module convolutional neural networks are used to form the final multiresidual module stacked convolutional neural network. The neural network model proposed in this paper achieves high accuracy of 89.5% and 88.2% on the upper arm and lower arm, respectively, so the method in this paper reduces the influence of occlusion on the athlete’s posture estimation to a certain extent. Through the experiments, it can be seen that the proposed multiresidual module stacked convolutional neural network-based method for athlete pose estimation in sports game videos further improves the accuracy of athlete pose estimation in sports game videos.

With the development of network and multimedia technology, multimedia communication has attracted the attention of researchers. Image encryption has become an urgent need for secure multimedia communication. Compared with the traditional encryption system, encryption algorithms based on chaos are easier to implement, which makes them more suitable for large-scale data encryption. The calculation method of image encryption proposed in this paper is a combination of high-dimensional chaotic systems. This algorithm is mainly used for graph mapping and used the Lorenz system to expand and replace them one by one. Studies have shown that this calculation method causes mixed pixel values, good diffusion performance, and strong key performance with strong resistance. The pixel of the encrypted picture is distributed relatively random, and the characteristics of similar loudness are not relevant. It is proved through experiments that the above calculation methods have strong safety performance.