Non-overlapping Blocks Research Articles

Blind and safety-enhanced dual watermarking algorithm with chaotic system encryption based on RHFM and DWT-DCT

This study proposes a novel blind watermarking algorithm based on radial harmonic Fourier moments (RHFMs) as well as discrete wavelet transform and discrete cosine transform (DWT-DCT) to achieve copyright protection. A non-adjacent coupled map lattice chaotic system was used to improve the safety in the watermark embedding process. First, a zero-watermark was obtained by performing DCT on the original image. Meanwhile, the direction information of the origin was calculated and embedded in the RHFM domain. During normal watermark embedding, the image-embedded direction information was transformed by a one-level DWT and the low-frequency sub-band was divided into 8×8 non-overlapping blocks. Each block was transformed by DCT, and two low-frequency coefficients were selected to embed the encrypted watermark. Experimental results demonstrated that the proposed algorithm is robust against rotation and other attacks. The analysis also showed that the proposed algorithm satisfies the invisibility of watermarking algorithms and has good security performance.

Nonoverlapping block smoothers for the Stokes equations

AbstractOverlapping block smoothers efficiently damp the error contributions from highly oscillatory components within multigrid methods for the Stokes equations but they are computationally expensive. This paper is concentrated on the development and analysis of new block smoothers for the Stokes equations that are discretized on staggered grids. These smoothers are nonoverlapping and therefore desirable due to reduced computational costs. Traditional geometric multigrid methods are based on simple pointwise smoothers. However, using multigrid methods to efficiently solve more difficult problems such as the Stokes equations leads to computationally more expensive smoothers, for example, overlapping block smoothers. Nonoverlapping smoothers are less expensive, but have been considered less efficient in the literature. In this paper, we develop new nonoverlapping smoothers, the so‐called triad‐wise smoothers, and show their efficiency within multigrid methods to solve the Stokes equations. In addition, we compare overlapping and nonoverlapping smoothers by measuring their computational costs and analyzing their behavior by the use of local Fourier analysis.

Digital image forensic using deep flower pollination with adaptive Harris hawk optimization

This paper presents a new segmentation algorithm and deep learning concept for innovative copy-move image forgery identification. In this work, the segmentation algorithm uses a new Adaptive Harris Hawk Optimization (AHHO) algorithm. The host image is segmented into irregular and non-overlapping blocks, named as Image Blocks (IB). Here the segmented image is compressed with a Discrete Cosine Transform (DCT). After that, the Zernike moments and Gabor filter-based features extraction techniques are processed to extract the Block Features (BF). At last, the tampered portions are classified with the hybrid Deep Neural Network (DNN) and Flower Pollination Algorithm (FPA) for forgery classification. This novel deep learning algorithm reduces the learning complexities and improves detection accuracy. The compression attacks are removed with anti-forensic blocking artefact removal concept. The experiments are conducted on the Benchmark dataset, CoMoFoD, and GRIP. For the benchmark dataset, precision, recall and F1 values are 91.27,100.0 and 96.07, respectively. For CoMoFoD dataset, the values of precision, recall and F1 are 92.57, 98.0 and 93.05, respectively. For GRIP dataset, the values of precision, recall and F1 are 97.02, 98.0 and 93.05, respectively. The implementation outcomes demonstrated that the proposed scheme is most effective in copy-move forgery recognition than the existing forgery detection approaches.

Information Set based Local Directional Number for Face Recognition

Many algorithms were proposed on face recognition based on the Holistic method, Feature-based method, and also more recently based on local texture patterns. Few local texture patterns utilize the positions of the intensity values like Local Directional Pattern and Local Directional number for obtaining the knowledge (features). This paper proposes the new features based on positions of intensity values and the intensity values in the patch of an image to compute membership function value. The information set concept is used to compute the features that are non-overlapping blocks to restrict the number of features. The proposed method is tested with benchmark databases like ORL and Sheffield and Yale. The classification of the subjects was done with Support Vector Machine (SVM) and K-nearest neighbour Classifier to validate the results. Bio-metric performance curves like Receiver operating Characteristics (ROC) and K-fold validation test is performed. The experimental result shows that the accuracy of recognition has improved over the previously mentioned methods.

Inspection of textile fabrics based on the optimal Gabor filter

Defect detection and quality control play an important role in the textile industry. In this paper, an automatic algorithm based on the optimal Gabor filter is proposed for real-time inspection of textile fabrics. The Cuckoo optimization algorithm is adopted to optimize the parameters of the Gabor filter. Also, an adaptive local binarization method is proposed, which enhances the performance of our algorithm. In order to locate the defects, the filtered image is divided into non-overlapping blocks. Then, the candidate defective blocks are binarized using adaptive thresholds, which are determined by blocks statistics. The performance of the proposed algorithm is evaluated through different types of fabrics in the TILDA database and an online Fabric Stain Dataset. The experimental results demonstrate the efficiency of the proposed method in detecting defects on the plain, regular and irregular patterned fabrics. Furthermore, the comparative results are provided to show the robustness of the proposed method.

Reversible data hiding in encrypted images with separability and high embedding capacity

In this paper, a high-capacity reversible data hiding (RDH) scheme for encrypted images with separability is proposed. The image is first divided into non-overlapping blocks, and each block is encrypted with the same random value. The advantage is that the correlation between adjacent pixels can be preserved. Utilizing the preserved correlation, the prediction difference in encrypted domain is exactly the same as that of plaintext domain, so that the separability can be achieved. Without accessing the original image content, the data-hider can embed additional data into encrypted image through histogram shifting and difference expansion. At the receiving end, the embedded additional data and the original image can be recovered without any error in separable manner. Experimental results are presented to demonstrate the feasibility and efficiency of the proposed scheme.

Nonoverlapping Block Stratified Random Sampling Approach for Assessment of Stationarity

Assessment of stationarity in any hydroclimatic time series is a vital task for hydrologic design and climate change assessments. Methods that rely on the presence of statistically significant trends or change points to derive inferences about stationarity may fail to check for all the time-invariant characteristics of time series. An approach that uses nonoverlapping stratified random sampling blocks of time series and nonparametric tests is proposed and evaluated in this study to assess stationarity. Chronologically continuous data from contiguous blocks are evaluated using two-sample and multisample nonparametric tests for the assessment of distributional, median and variance similarity, invariance of statistical moments, and autocorrelation at several lags. Explicit methods for the evaluation of different characteristics of time series are also developed to assess the two forms (viz., weak and strict) of stationarity. The multiple test evaluations are weighted using the analytical hierarchy process (AHP) to draw conclusions about stationarity. The proposed approach is tested using several real-world and synthetically generated hydroclimatic data sets through extensive simulations. Stationarity assessments derived from nonparametric trend and unit root tests have been compared to those from the approach developed. Results from this study point to the correct identification of weak and strict stationarity in real-world hydroclimatic time series compared to similar evaluations from unit root and trend tests. The robustness of the approach is also confirmed by an accurate assessment of the stationarity of several synthetic time series representing various hydroclimatic processes at different time scales. The proposed approach is a conceptually simple and superior alternative to trend and unit root tests since it provides a comprehensive assessment of stationarity using multiple nonparametric tests.

Color Image Self-Recovery and Tampering Detection Scheme Based on Fragile Watermarking with High Recovery Capability

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

Change Detection in Landsat Images Using Unsupervised Learning and RBF-Based Clustering

Change detection is one of the extensively investigated problems in the field of remote sensing. Generally, it involves the analysis of multitemporal remote sensing images of the same sensor or different sensors. The nonnormalizable radiometric differences, which are added due to environmental interferences, could lead to increase the intra-class variability in multitemporal images, and hence, the change detection problem would be nonlinear. To cope with these issues, a novel unsupervised manifold learning based change detection technique is proposed, which discovers the nonlinear structure of data hidden in original space. The proposed framework consists of two processing stages. In the first stage, a novel orthogonal unsupervised discriminant projection (OUDP) based technique is proposed for feature extraction. The orthogonal basis vectors, obtained from OUDP, are able to provide discriminant features. In addition, in the second stage, a novel radial-basis function based clustering is presented, which yields the better clustering than the single clustering approach. Moreover, an extended kernel-weighted OUDP technique is also presented, which improves the performance of OUDP technique. Particularity of the proposed framework resides on following three things. First one is the creation of eigenvector space by using the nonoverlapping blocks of the image. Second one is the extraction of features by exploiting the local neighborhood information around each pixel, this generates clear or spot free change maps. The last one is the RBF stage of clustering technique, which greatly improves the classification of the unchanged and changed pixels. Experimental results on multispectral images of different sensors validate the effectiveness of the proposed method.

A Novel HDR Image Zero-Watermarking Based on Shift-Invariant Shearlet Transform

In this paper, a novel high dynamic range (HDR) image zero-watermarking algorithm against the tone mapping attack is proposed. In order to extract stable and invariant features for robust zero-watermarking, the shift-invariant shearlet transform (SIST) is used to transform the HDR image. Firstly, the HDR image is converted to CIELAB color space, and the L component is selected to perform SIST for obtaining the low-frequency subband containing the robust structure information of the image. Secondly, the low-frequency subband is divided into nonoverlapping blocks, which are transformed by using discrete cosine transform (DCT) and singular value decomposition (SVD) to obtain the maximum singular values for constructing a binary feature image. To increase the watermarking security, a hybrid chaotic mapping (HCM) is employed to get the scrambled watermark. Finally, an exclusive-or operation is performed between the binary feature image and the scrambled watermark to compute robust zero-watermark. Experimental results show that the proposed algorithm has a good capability of resisting tone mapping and other image processing attacks.

Robust copy-move forgery detection using modified superpixel based FCM clustering with emperor penguin optimization and block feature matching

Digital images face widespread tampering through a copy-move attack, wherein objects are added or removed from an image to create the copy-move forged image. This paper proposes a robust Copy Move Forgery Detection (CMFD) scheme by integrating both block-based and keypoint-based CMFD techniques. The input image is first segmented into non-overlapping blocks using a modified FCM clustering algorithm based on superpixels. The neighboring and similar superpixels influences are encompassed into FCM, and Emperor Penguin Optimization (EPO) is used to optimize the influential degree, which increases the segmentation performance. Gabor filter is applied to extract the features from each block. The Block features are then matched with each other to show the suspected forgery regions. The experimental results illustrate that the proposed technique attains better performance in terms of precision, recall, F1-score, and E-measure. It accomplishes excellent forgery detection outcomes compared with other existing CMFD processes, even under different perplexing circumstances.

A Statistical Color Image Watermarking Scheme Using Local QPCET and Cauchy–Rayleigh Distribution

Based on local quaternion polar complex exponential transform (QPCET) and Cauchy–Rayleigh distribution, we propose a statistical color image watermarking scheme in this paper, which can achieve the trade-off among imperceptibility, robustness and data payload. Our color image watermarking scheme consists of two parts, namely embedding and detecting. In the embedding process, we divide the color host image into non-overlapping blocks and compute the local QPCET of color image blocks and then insert the watermark signal into the robust local QPCET magnitudes through multiplicative approach. In the detecting phase, robust local QPCET magnitudes are firstly modeled by employing the Cauchy–Rayleigh distribution, where the statistical properties of local QPCET magnitudes are captured accurately. Then, genetic algorithm-based maximum likelihood approach is introduced to estimate the statistical parameters of Cauchy–Rayleigh distribution model. And finally a color image watermark detector for multiplicative watermarking is developed using Cauchy–Rayleigh distribution and locally most powerful test. Also, we utilize the Cauchy–Rayleigh statistical model to derive the closed-form expressions for the watermark detector. Experimental results on some standard test images and comparison with well-known existing methods demonstrate the efficacy and superiority of the proposed scheme.

SAE based unified double JPEG compression detection system for Web image forensics

PurposeRecently, the double joint photographic experts group (JPEG) compression detection tasks have been paid much more attention in the field of Web image forensics. Although there are several useful methods proposed for double JPEG compression detection when the quantization matrices are different in the primary and secondary compression processes, it is still a difficult problem when the quantization matrices are the same. Moreover, those methods for the different or the same quantization matrices are implemented in independent ways. The paper aims to build a new unified framework for detecting the doubly JPEG compression.Design/methodology/approachFirst, the Y channel of JPEG images is cut into 8 × 8 nonoverlapping blocks, and two groups of features that characterize the artifacts caused by doubly JPEG compression with the same and the different quantization matrices are extracted on those blocks. Then, the Riemannian manifold learning is applied for dimensionality reduction while preserving the local intrinsic structure of the features. Finally, a deep stack autoencoder network with seven layers is designed to detect the doubly JPEG compression.FindingsExperimental results with different quality factors have shown that the proposed approach performs much better than the state-of-the-art approaches.Practical implicationsTo verify the integrity and authenticity of Web images, the research of double JPEG compression detection is increasingly paid more attentions.Originality/valueThis paper aims to propose a unified framework to detect the double JPEG compression in the scenario whether the quantization matrix is different or not, which means this approach can be applied in more practical Web forensics tasks.

Self-adaptive step firefly algorithm based robust watermarking method in DWT-SVD domain

Digital image watermarking, a data hiding method has become widespread to prohibit illegal use of personal files lately. In this study, a robust Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) based technique is proposed for copyright protection. The cover image is firstly decomposed into sub-bands by DWT. Low frequency sub-band is then divided into non-overlapping blocks. Blocks where watermark will be embedded are selected depending on their standard deviation values. Selected blocks are transformed to U, S, and V matrices by SVD. Watermark is embedded into the second row value in the first column of U component obtained by SVD. Embedding scaling factor is determined by Self-Adaptive Step Firefly Algorithm (SASFA) to balance robustness and transparency that are contradictory to each other. Firefly Algorithm (FA) is a simple, easy to implement, and flexible algorithm but it can pass over the global optimum or get trapped at local optimum. Therefore, SASFA, which constitutes the next step of each firefly based on its previous and present situations is used for global exploration of the solution space. Fibonacci-Lucas Transform (FLT) is applied to binary watermark to provide the security of watermarking scheme. Achieved scrambled watermark bits are used in embedding process. Performance of the proposed scheme is measured by using Bit Error Rate (BER), Normalized Correlation (NC), and Peak Signal-to-Noise Ratio (PSNR). Experimental results prove that the method has high robustness level against attacks and a good visual quality.

A Novel Improved Reversible Visible Image Watermarking Algorithm Based on Grad-CAM and JND

With the rapid access convenience of content brought by 5G technology, the integrity protection of content becomes more important. The reversible visible watermarking algorithm has attracted more attention due to its effective content protection. In this paper, a novel improved reversible visible image watermarking scheme based on gradient-weighted class activation mapping (Grad-CAM) and the just noticeable difference (JND) model has been presented. The proposed region of interest (ROI) selection strategy is used to locate the main protected body of images for watermark embedding. Divide the watermark and ROI into nonoverlapping blocks in the same way and then embed the classified two types of watermark blocks into corresponding ROI blocks with the JND model. The optimal bit positions for watermark embedding can be selected adaptively with JND threshold and achieve the tradeoff between the watermark visibility and watermarked image quality. For lossless image recovery and watermark extraction, the recovery information is reversibly hidden into watermarked image. In the experiments, the same process of grayscale images is used to each channel separately for color images watermarking. Besides, there are six aspects in this paper to estimate the proposed scheme; with the comparison to other reversible visible watermarking schemes, experimental results demonstrate the effectiveness of our proposed scheme.

Secured image steganography based on Catalan transform

Transform domain steganography methods are always preferable than spatial domain methods to the research community due to following advantages: better feature identification, improved security and higher robustness. In this paper, an integer sequence named Catalan Transform (CT) has been exploited in the image steganography domain. At the outset, the cover image is decomposed into 2 × 2 non-overlapping blocks in row major order. Then, each such block i.e., 4-pixel group is converted into transform domain using CT. Secret bits are embedded into the transformed components in varying proportions which facilitates us to achieve a payload in the range of 1 to 4 bpp. Inverse Catalan Transform (ICT) is applied over transformed cum embedded quadruples to generate the stego-pixels in spatial domain. Successive embedding operation over an entire image ensures the formation of stego-image. Experimental results confirm that the Peak Signal to Noise Ratio (PSNR) values obtained by the proposed method are always above the acceptable level (i.e., 30 dB) and at the same time, it outperforms many state-of-the-art methods. Other metrics such as Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM), Normalized Cross Correlation (NCC) and Global histograms with similarity measure have also been computed which prove the effectiveness of the proposed scheme. The robustness of the stego-images has further been tested using the StegExpose tool and it is found that the tool is unable to detect the presence of secret data. The code of this work is available at: https://github.com/Souradeep150/Catalan-based-Steganography .

Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition.

Single-Sample Face Recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, mainly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper discusses the relevance of an original method for SSFR, called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF), which exploits several kinds of features, namely, local, regional, global, and textured-color characteristics. First, the MB-C-BSIF method decomposes a facial image into three channels (e.g., red, green, and blue), then it divides each channel into equal non-overlapping blocks to select the local facial characteristics that are consequently employed in the classification phase. Finally, the identity is determined by calculating the similarities among the characteristic vectors adopting a distance measurement of the K-nearest neighbors (K-NN) classifier. Extensive experiments on several subsets of the unconstrained Alex and Robert (AR) and Labeled Faces in the Wild (LFW) databases show that the MB-C-BSIF achieves superior and competitive results in unconstrained situations when compared to current state-of-the-art methods, especially when dealing with changes in facial expression, lighting, and occlusion. The average classification accuracies are 96.17% and 99% for the AR database with two specific protocols (i.e., Protocols I and II, respectively), and 38.01% for the challenging LFW database. These performances are clearly superior to those obtained by state-of-the-art methods. Furthermore, the proposed method uses algorithms based only on simple and elementary image processing operations that do not imply higher computational costs as in holistic, sparse or deep learning methods, making it ideal for real-time identification.

Tamper Detection and Self-Recovery of Medical Imagery for Smart Health

Social Internet of Things (SIoT) is one of the emerging research areas which integrates the Internet of Things (IoT) with social networking concepts. It envisages better information sharing, by overcoming the scalability issues in IoT. It is supposed to overhaul the smart health industry, as it is a flexible compared to conventional IoT based solutions. It however poses many challenges with data authentication and privacy, being a couple of such issues that need to be addressed. In this paper, we present an efficient watermarking method for tamper detection, localization, and self-recovery of medical images for SIoT-based smart health networks. The cover image is initially divided into four equal parts, with each part progressively divided into 4 × 4 non-overlapping blocks. A watermark is prepared from a group of randomly chosen four, 4 × 4 blocks, determined by chaotic mapping. The information is embedded into 4 sub-blocks mapped utilizing chaotic sequence such that the restoration process can be carried out even if three sub-blocks out of four containing the information, get tampered with. The proposed technique is found to restore almost 1/3rd of the tampered image and produces a high-quality image with the average Peak Signal-to-Noise Ratio value of above 33 dB after the restoration process. The performance of the proposed method for various collage attacks shows its efficacy over other state-of-the-art techniques making it a potential candidate for SIoT driven smart health systems.

Reversible Data Hiding in Encrypted Images Based on Block-Wise Multi-Predictor

Reversible data hiding of encrypted images (RDHEI) is a technology where after the original image is encrypted with a key, secret data can be embedded in the encrypted image without knowing the information of the original image. In recent years, RDHEI has attracted much attention due to its security and effectiveness, and many RDHEI methods have been proposed. However, the capacity of embedded data is still not high enough. In this paper, a high-capacity RDHEI algorithm based on block-wise multi-predictor, and improved Huffman coding is proposed. First, the original image is divided into non-overlapping blocks. To better exploit the spatial correlation of the original image, 16 prediction models are used to predict the current pixel value from the neighboring pixels. All the pixels in one block use the same prediction model. The improved Huffman encoding is used to compress the prediction error image and the optimal prediction model determined by the sum of the absolute prediction errors, which makes more room for data embedding. The experimental results show that our method is superior to some of the latest algorithms in terms of achieving a higher embedding capacity.

Distributed Video Compressed Sensing Secondary Reconstruction Based on Inter-Frames Similarity Structure

The emergence of distributed video compressed sensing technology has effectively reduced the computational cost and power consumption of the video system. As the core content of the DCVS research, reconstruction algorithms are mostly focused on improving the reconstruction quality of CS frames, while neglecting the reconstruction quality of the key frame. The key frame is not fully utilized as well. Focus on these problems, the paper proposes a distributed video compressed sensing secondary reconstruction algorithm based on similar structure between frames. In order to eliminate the influence of non-overlapping block CS that may introduce noise and block effects on key frames, firstly, the fractional total variation algorithm is used to initially reconstruct the key frames, which improves the reconstruction quality while reducing the computational complexity. Then, a CS frame secondary reconstruction algorithm based on structural similarity is proposed, which uses SSIM as the matching criterion to search for matching block groups similar to the current frame from multiple reference frames, as a regular term to achieve the secondary reconstruction of CS frames. The experimental results show that the overall performance of the proposed algorithm is better than the current advanced DCVS method. The research results of the thesis can be used for reliable compressed sensing reconstruction of video and image.