Correlation Of Pixels Research Articles

Low dimensional DCT and DWT feature based model for detection of image splicing and copy-move forgery

Digital images are being used as a prominent carrier of visual information in this age of digitization. Images become more and more omnipresent in everyday life. The images can be easily manipulated due to the accessibility of many internet tools and advanced software. Previously many techniques have been developed to authenticate the images. But all the previous techniques have high dimension of feature vectors. Here, a low dimensional DCT and DWT based features have been introduced to authenticate the images. In this work, we are dealing with both the passive forgery (splicing and copy-move) simultaneously. Features are extracted through image statistics and pixel correlation from DCT and DWT domain. Ensemble classifier has been selected for training and testing. The classifier classifies whether the given images are forged or authentic. Further, it also classifies the forgery in spliced or copy-move. If there is copy-move, the proposed work also perform the region detection using a novel key-point based method. The proposed model gives good detection accuracy and high generalization capability which is independent of image formats. Experimental results demonstrate the performance of proposed work against different post-processing operations like scaling, rotation, and Gaussian noise. Also, the comparative results against different existing methods show the effectiveness of the proposed model.

GSAPSO-MQC:medical image encryption based on genetic simulated annealing particle swarm optimization and modified quantum chaos system

Due to the large amount of image information data, high redundancy and high pixel correlation, the traditional medical image encryption algorithm is easy to be attacked by chosen plaintext. Therefore, a new medical image encryption algorithm combining genetic simulated annealing particle swarm optimization and modified quantum chaos system is proposed to obtain better security performance. Firstly, an improved quantum chaotic system is used to generate the key stream. Then the selection and cross operation of genetic algorithm are used to process the plaintext image. The optimal sequence generated by simulated annealing algorithm is used to scramble the image. Meanwhile, the particle swarm optimization (PSO) algorithm is introduced into the simulated annealing mechanism. The initial temperature is set according to the optimal fitness value of the initial population. Metropolis is used to optimize the generation of individual optimal position and global optimal position, and the inertial weight parameters of PSO algorithm are optimized to avoid particles falling into local optimal in the optimization process and improve the convergence speed of the algorithm. Through these three operations, the histogram of the scrambled image can be equalized to resist statistical attack. Experimental results and performance analysis show that the encryption system proposed in this paper can resist many typical attacks such as histogram analysis, correlation analysis, differential attack and violent attack, and has high security and encryption efficiency. Compared with other encryption methods, the encryption efficiency of our proposed method has improved by approximately 10%.

Trajectory control and image encryption using affine transformation of lorenz system

This paper presents a generalization of chaotic systems using two-dimensional affine transformations with six introduced parameters to achieve scaling, reflection, rotation, translation and/or shearing. Hence, the location of the strange attractor in space can be controlled without changing its chaotic dynamics. In addition, the embedded parameters enhance the randomness and sensitivity of the system and control its response. This approach overpasses performing the transformations as post-processing stages by applying them on the resulting time series. Trajectory control through dynamic parameters is demonstrated. Simulation results validate the proposed analysis for both the simplest and Lorenz chaotic systems. An image encryption scheme is implemented using transformed Lorenz system resulting in a more secure encryption scheme in comparison to Lorenz and other recent related works. The scheme exhibits good performance when assessed using the PRNG properties, encrypted image histogram and its uniformity through chi square test, pixel correlation, Mean Squared Error (MSE), entropy, Peak Signal-to-Noise Ratio (PSNR), the National Institute of Standards & Technology (NIST) test, key space, key sensitivity, resistance to differential, ciphertext-only, known plaintext, and chosen plaintext attacks, robustness against noise and computation time.

Hyperspectral Anomaly Detection via Image Super-Resolution Processing and Spatial Correlation

Anomaly detection is a key problem in hyperspectral image (HSI) analysis with important remote sensing applications. Traditional methods for hyperspectral anomaly detection are mostly based on the distinctive statistical features of the HSIs. However, the anomaly-detection performance of these methods has been negatively impacted by two major limitations: 1) failure to consider the spatial pixel correlation and the ground-object correlation and 2) the existence of the mixing pixels caused by both lower spatial resolution and higher spectral resolution, which leads to higher false-alarm rates. In this article, these two problems are largely solved through a novel hyperspectral anomaly-detection method based on image super-resolution (SR) and spatial correlation. The proposed method encompasses two innovative ideas. First, based on the spectral variability in the anomaly targets, an extended linear mixing model can be obtained with more accurate ground-object information. Then, image SR is used to improve the spatial resolution of the HSIs by injecting the ground-object information from the mixing model. This alleviates the effect of mixed pixels on anomaly detection. Second, spatial correlation is exploited jointly with the global Reed-Xiaoli (GRX) method and the ground-object correlation detection for anomaly detection. Experimental results show that the proposed method not only effectively improves the hyperspectral spatial resolution and reduces the false-alarm rate but also increases the detectability with the spatial correlation information. Furthermore, the results for the real HSIs demonstrate that the proposed method achieves higher rates of anomaly detection with lower false-alarm rates.

Brain Tumor Detection Based on Multilevel 2D Histogram Image Segmentation Using DEWO Optimization Algorithm

Brain tumor detection from magnetic resonance (MR)images is a tedious task but vital for early prediction of the disease which until now is solely based on the experience of medical practitioners. Multilevel image segmentation is a computationally simple and efficient approach for segmenting brain MR images. Conventional image segmentation does not consider the spatial correlation of image pixels and lacks better post-filtering efficiency. This study presents a Renyi entropy-based multilevel image segmentation approach using a combination of differential evolution and whale optimization algorithms (DEWO) to detect brain tumors. Further, to validate the efficiency of the proposed hybrid algorithm, it is compared with some prominent metaheuristic algorithms in recent past using between-class variance and the Tsallis entropy functions. The proposed hybrid algorithm for image segmentation is able to achieve better results than all the other metaheuristic algorithms in every entropy-based segmentation performed on brain MR images.

A Power Efficient Image Sensor Readout With On-Chip $\delta$ -Interpolation Using Reconfigurable ADC

In this paper, a low-power readout using reconfigurable cyclic ADC for CMOS image sensors is proposed. It reduces the total number of pixels to be read by taking advantage of pixel correlation. The required number of ADC operations is reduced, resulting in power saving. In contrast to the existing pixel correlation-based approaches, which focus only on the intensity differences, in the proposed method, the polarity of the differences is also taken into account. It helps in preserving fine edges representing features such as texture. The discarded or unread pixels are interpolated on-chip while reconfiguring the ADC input range according to the interpolation step size. Furthermore, this reduces the number of ADC conversion cycles by 25% to 50% for interpolation steps of 16 LSB and 64 LSBs, respectively. The ADC is designed and fabricated in UMC 180-nm CMOS technology, and the proposed method is verified for standard test images. The reconstructed images incorporating ADC non-linearities result in average Pratt’s FoM values of 0.88, 0.86, and 0.81 for 60%, 70%, and 74% compression, respectively. The corresponding best values achieved by the existing approaches are 0.86, 0.80, and 0.77, respectively. The improvement in FoM is observed due to the consideration of polarity information. The proposed technique results from 33% to 50% power saving for 80% compression in $512\times512$ image, using reconfigurable ADC. Therefore, it is suitable for a power efficient CMOS sensor design.

A Novel Privacy Approach of Digital Aerial Images Based on Mersenne Twister Method with DNA Genetic Encoding and Chaos

Aerial photography involves capturing images from aircraft and other flying objects, including Unmanned Aerial Vehicles (UAV). Aerial images are used in many fields and can contain sensitive information that requires secure processing. We proposed an innovative new cryptosystem for the processing of aerial images utilizing a chaos-based private key block cipher method so that the images are secure even on untrusted cloud servers. The proposed cryptosystem is based on a hybrid technique combining the Mersenne Twister (MT), Deoxyribonucleic Acid (DNA), and Chaotic Dynamical Rossler System (MT-DNA-Chaos) methods. The combination of MT with the four nucleotides and chaos sequencing creates an enhanced level of security for the proposed algorithm. The system is tested at three separate phases. The combined effects of the three levels improve the overall efficiency of the randomness of data. The proposed method is computationally agile, and offered more security than existing cryptosystems. To assess, this new system is examined against different statistical tests such as adjacent pixels correlation analysis, histogram consistency analyses and its variance, visual strength analysis, information randomness and uncertainty analysis, pixel inconsistency analysis, pixels similitude analyses, average difference, and maximum difference. These tests confirmed its validity for real-time communication purposes.

Generalized switched synchronization and dependent image encryption using dynamically rotating fractional-order chaotic systems

This paper presents a generalization, attractor control and multi-scroll generation method for fractional-order chaotic systems through rotation transformation. A novel synchronization-dependent colored image encryption and secure communication scheme is also proposed. The systems with dynamic rotation angle fit successfully in a generalized dynamic switched synchronization scheme. Dynamic control switches specify whether the system acts as a master or slave. Dynamic scaling factors determine whether the master is a single system or a combination of two or more systems. Simulation results validate successful synchronization when rotating fractional-order systems act as slave or master or both. The proposed synchronization is suitable for one-to-one, one-to-many, mutual interconnection and role switching communication applications. The proposed synchronization-dependent image encryption scheme performs rotation angle modulation using the plaintext and XOR logic operation for plaintext image substitution. The performance of the chaotic systems as Pseudo-Random Number Generators (PRNGs) is validated using National Institute of Standards & Technology (NIST). Three synchronization scenarios are tested using Lena image. The performance of the scheme is evaluated through the encrypted image, histogram and its uniformity through chi square test, pixel correlation, Mean Squared Error (MSE), entropy, Peak Signal-to-Noise Ratio (PSNR), key sensitivity, resistance to brute force, differential and other security attacks.

High Capacity Reversible Data Hiding in Encrypted Image Based on Intra-Block Lossless Compression

The cover image is generally encrypted by a stream cipher in existing reversible data hiding in encrypted image (RDHEI) methods. As pixel correlation is seriously damaged, more than one pixel should be employed to carry one bit such that the quite limited capacity is achieved. To overcome this issue, a new RDHEI method with high capacity, that preserves pixel correlation and exploits it to vacate embedding room, is proposed in this paper. First, we propose a block-level encryption scheme which combines block-level stream cipher and block-level permutation, and all blocks are classified into usable blocks (UBs) and unusable blocks (NUBs) by preserving the correlation of pixels in blocks. Then, UB is reconstructed to vacate room for data embedding, because the pixels in blocks share the same most significant bits (MSBs). To ensure reversibility, the number of NUBs between current UB and the previous one is also embedded along with additional data, and the blocks are rearranged in a reversible way such that UBs are always in front of NUBs. Experimental results show that not only the embedding capacity is significantly improved but also the hidden data can be losslessly extracted, and the cover image can be perfectly recovered.

An ASCII based effective and multi-operation image encryption method

The main challenge for American Standard Code for Information Interchange (ASCII) based image encryption methods is to increase security with less computational cost. Earlier, well known image encryption techniques are not only deficient in performance but also lacked in embedding of confidential data in an image using ASCII based methods. Therefore, this study aims to contribute an ASCII based efficient and secure image encryption method having additional feature of embedding confidential data in an image. Moreover, the embedding process is not static but it is dynamic as compared to the existing work. The novelty of proposed method encompasses optimal security, effective encryption speed and randomized embedding of secret data in an image even of having multi-operations. The proposed method was evaluated experimentally with variety of tests such as Pixel correlation analysis, Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI), Histogram, Entropy, Peak Signal to Noise Ratio (PSNR), and Mean Square Error (MSE) including encryption decryption time. Thus the experimental results show that the proposed method is optimally secure and outperformed in image encryption as well as in randomized embedding of confidential data than the existing techniques.

A new structure of spline wavelet transform based on adaptive directional lifting for efficient image coding

We present in this paper a modified structure of a polynomial spline wavelet transform based on adaptive directional lifting for image compression. The proposed method not only uses the polynomial splines as a tool for the construction of the appropriate filters seeing its efficiency as compared to other filters like the biorthogonal 9/7, but also adapts far better to the image-orientation features by carrying out a lifting-based prediction in local windows in the direction of high pixel correlation. The main purpose of this article is then to integrate the coefficients calculated by the best spline filter order into the adaptive directional lifting. The new method is designed to further reduce the magnitude of the high-frequency wavelet coefficients and preserve the detailed information of the original images more effectively. The numerical results demonstrate the efficiency of the proposed approach over the traditional lifting-based spline wavelet transform and the adaptive directional lifting with respect to both objective and subjective criteria for image compression applications.

Distinguishing Computer-Generated Images from Natural Images Using Channel and Pixel Correlation

With the recent tremendous advances of computer graphics rendering and image editing technologies, computergenerated fake images, which in general do not reflect what happens in the reality, can now easily deceive the inspection of human visual system. In this work, we propose a convolutional neural network (CNN)-based model to distinguish computergenerated (CG) images from natural images (NIs) with channel and pixel correlation. The key component of the proposed CNN architecture is a self-coding module that takes the color images as input to extract the correlation between color channels explicitly. Unlike previous approaches that directly apply CNN to solve this problem, we consider the generality of the network (or subnetwork), i.e., the newly introduced hybrid correlation module can be directly combined with existing CNN models for enhancing the discrimination capacity of original networks. Experimental results demonstrate that the proposed network outperforms state-of-the-art methods in terms of classification performance. We also show that the newly introduced hybrid correlation module can improve the classification accuracy of different CNN architectures.

Reversible Data Hiding in Encrypted Image Based on Multi-MSB Embedding Strategy

In this paper, a reversible data hiding method in encrypted image (RDHEI) is proposed. Prior to image encryption, the embeddable pixels are selected from an original image according to prediction errors due to adjacent pixels with strong correlation. Then the embeddable pixels and the other pixels are both rearranged and encrypted to generate an encrypted image. Secret bits are directly embedded into the multiple MSBs (most significant bit) of the embeddable pixels in the encrypted image to generate a marked encrypted image during the encoding phase. In the decoding phase, secret bits can be extracted from the multiple MSBs of the embeddable pixels in the marked encrypted image. Moreover, the original embeddable pixels are restored losslessly by using correlation of the adjacent pixels. Thus, a reconstructed image with high visual quality can be obtained only when the encryption key is available. Since exploiting multiple MSBs of the embeddable pixels, the proposed method can obtain a very large embedding capacity. Experimental results show that the proposed method is able to achieve an average embedding rate as large as 1.7215 bpp (bits per pixel) for the BOW-2 database.

Face anti-spoofing by identity masking using random walk patterns and outlier detection

Existing architectures used in face anti-spoofing tend to deploy registered spatial measurements to generate feature vectors for spoof detection. This means that the ordering or sequence in which specific statistics are computed cannot be changed, as one moves from one facial profile to another. While this arrangement works in a person-specific setting, it becomes a major drawback when single-sided training is done based on the natural face class alone. To mitigate subject identity linked content interference within the anti-spoofing frame, we propose a identity-independent architecture based on random correlated scans of natural face images. The same natural face image can be scanned multiple times through independent correlated random walks before deriving simple differential features on the 1D scanned vectors. This proposed frame tends to capture the pixel correlation statistics with minimal content interference and shows great promise, particularly when trained on natural face sets, using a one-class support vector machine and cross-validated on other databases. Performance measured in terms of EER for detection of spoof face is found to be $$3.8291\%$$ with proposed random scan features, and $$2.02\%$$ with auto-population samples for inter database.

Image encryption with cross colour field algorithm and improved cascade chaos systems

In this study, the critical factor of the chaos system has been analysed to improve the randomicity of the encryption keyspace. Several chaos systems have been integrated together with a linear function to form a much more efficient key sequence generator. This study also presents the cross colour field confusion method which scrambles the pixels among the R G B colour matrixes. In this way, the range of the pixels scrambling map is extended to three matrixes compared to the traditional pixels scrambling schemes which do pixels scrambling in its own colour matrix. The experiment results show that the improved cascade system has better bifurcation diagrams. The cross colour field diffusion algorithm makes the encrypted image has little pixel correlation and no information leakage. The experiment results justify that the novel cross colour confusion scheme with improved chaos systems has a good ability to resist brute force attack, known plain attack, chosen plain text attack, chosen ciphertext attack and differential attack. The security analysis demonstrates that the proposed approach has satisfactory properties in image encryption.

An effective image compression–encryption scheme based on compressive sensing (CS) and game of life (GOL)

At present, information entropies of cipher images gotten by some CS-based image cryptosystems are lower than 7, which make them vulnerable to entropy attack. To cope with this problem, we propose a novel image compression–encryption method based on compressive sensing (CS) and game of life (GOL). Encryption architecture of permutation, compression and diffusion is utilized. Firstly, a plaintext-dependent game-of-life-based scrambling method is presented to shuffle the sparse coefficient matrix of plain image, and the permutation matrix is constructed by rules of GOL, which may effectively reduce the adjacent pixel correlation and enhance the scrambling effect. Secondly, the confused matrix is compressed by CS and diffused using a key matrix to get the cipher image. Additionally, a five-dimensional (5D) memristive hyperchaotic system is used to generate chaotic sequences. They are utilized to construct measurement matrix, to generate initial cell matrix of GOL and to produce key matrix. Information entropy of plain image and external key parameters are combined to compute initial values of the hyperchaotic system. Therefore, our algorithm has high sensitivity to original image and it may resist against known-plaintext attack and chosen-plaintext attack. Experimental results and performance analyses demonstrate that the proposed encryption algorithm is effective to withstand various typical attacks, and it may be applied for image secure communication.

Pixel Value Ordering Based Reversible Data Hiding with Novel MPBS Strategy

Recently, Reversible Data Hiding (RDH) techniques has gained much attention in many sensitive fields such as remote sensing, archive management, military and medical image processing systems. This is due to the lossless data extraction ability of RDH schemes. The primary goal of RDH schemes is to achieve high embedding rates while maintaining the quality of cover objects. For achieving better performance, Pixel Value Ordering (PVO) based reversible data hiding schemes have been proposed. PVO refers to the process of ranking the pixels in blocks and then modifying the pixels according to some embedding rules/conditions. So far, the existing PVO techniques have considered neighborhood pixels at unit distance. In this paper, an improved RDH using block based PVO scheme is proposed which exploits the pixel correlation efficiently by increasing the block size and applying a novel Median Pixel based Block Selection Strategy (MPBS). When block size is increased, the ordering of pixels is changed after embedding. So, to extract the secret data in a lossless manner, the secret bits are swapped in accordance with their corresponding Stego pixels’ index order. Also, the overflow and underflow conditions are effectively handled using Location Map. Experimental results show the better performance of the proposed RDH technique with the existing technique.

Fast image encryption algorithm with high security level using the Bülban chaotic map

In the last decades, a big number of image encryption schemes have been proposed. Most of these schemes reach a high-security level, however, their slow speeds due to their complex process make them unusable in real-time applications. Motivated by this, we propose a new efficient and high-speed image encryption scheme based on the Bulban chaotic map. Unlike most of the existing schemes, we make a wisely use of this simple chaotic map to generate only a few numbers of random rows and columns. Moreover, to further increase the speed, we raise the processing unit from the pixel level to the row/column level. Security of the new scheme is achieved through a substitution-permutation network, where we apply a circular shift of rows and columns to break the strong correlation of adjacent pixels. Then, we combine the XOR operation with the Modulo function to mask the pixels values and prevent any leak of information. High-security tests and simulation analysis have been carried out to demonstrate that the scheme is extremely secure and highly fast for real-time image processing at 80 fps (frames per second).

A novel one-dimensional sine powered chaotic map and its application in a new image encryption scheme

With the rapid growth of data transmission technology, image encryption is becoming a widely discussed topic in the field of information security. In this paper, we introduce a novel one-dimensional sine powered chaotic system (1DSP). Evaluation of the proposed chaotic system indicates the existence of chaotic behavior with high sensitivity and randomness. We further present a new image encryption scheme using 1DSP (1DSP-IE). The proposed scheme uses the 1DSP for sequence addition to improve its security. Furthermore, row-by-row and column-by-column concepts are used for confusion and diffusion operations. This approach breaks the correlation of adjacent pixels by scrambling pixels out of their original rows and columns and applies bit-level pixel value manipulation for each row and then each column to spread any change in the plain image throughout the ciphered image. The simulation results of the proposed 1DSP-IE indicate an effective encryption and decryption process with good speed. In addition, the security analysis illustrates the ability of 1DSP-IE to provide a satisfying level of security in comparison with other image encryption schemes.

FPGA based generic RO TRNG architecture for image confusion

True Random Number Generator (TRNG) has become a central element of today’s secure communication. TRNGs developed through FPGA implementation have a significant role in a number of applications in multimedia communication. Ring Oscillator (RO) has been adopted extensively for developing device-independent TRNG structures. The proposed design is a generic TRNG architecture based on an arrangement of ROs with post-processing. This TRNG was initially tested on Altera Cyclone II FPGA which consumed only 64 inverters to produce good randomness. Also, the device-independent capability of this design has been verified by implementing it on other two FPGA families Xilinx Artix – 7 and Microsemi Smart Fusion2. True randomness was also verified by conducting restart experiment, and the statistical properties of TRNG were evaluated through entropy analysis and NIST SP 800–22 tests. The proposed TRNG yields 26.640650 Mbps as throughput with a sampling clock of 27 MHz. Going beyond the regular key generation utility, the evolving random bits of TRNG were packed suitably to perform confusion of grayscale images producing a near-zero correlation of pixels, thereby extending the application of TRNG to image encryption.