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Multimedia security has been extensively used in content protection, image authentication, data hiding and signal encryption. Similarly, transmission of biomedical data or information remotely for healthcare applications should be secure. One of the important medical signals that need to be transmitted to healthcare centers is the Electrocardiogram (ECG) signal. This paper is concerned mainly with ECG signal encryption for security applications. The paper presents three cryptosystems for ECG signal encryption based on the fusion of ECG signals with other masking signals that are rich in activities such as speech signals. The common thread between these cryptosystems is the operation on sample values of the ECG signal rather than adopting encoding and decoding schemes, and this saves much time and is more immune to both noise and hacking scenarios. The proposed cryptosystems are compared to the encryption technique that uses 1-D logistic map. The performances of the proposed cryptosystems are evaluated through simulation experiments in terms of histogram, structural similarity index, Signal-to-Noise Ratio (SNR), log-likelihood ratio, spectral distortion and correlation coefficient. It is clear from the experiments that the utilization of more levels of encryption increases the security.

Multimodality medical image fusion is the process of combining multiple images from single or multiple modalities of imaging. Medical image fusion methods are adopted to increase the quality of medical images by attaining the salient features in the fusion results. Hence, they raise the clinical applicability of medical images for appraisal and diagnosis problems. This purpose is achieved by capturing the complementary information presented in two or more images of different modalities in the fusion result. Medical image fusion is generally concerned with Magnetic Resonance Imaging (MRI), Magnetic Resonance Angiogram (MRA), Positron Emission Tomography (PET), Structural Positron Emission Tomography (SPET), Computerized Tomography (CT), and Single-Photon Emission Computed Tomography (SPECT) modalities. Each modality has its merits and drawbacks. This induces new fusion methods for merging information from multiple imaging modalities. Researchers have presented several methods for medical image fusion, and these methods achieved good results. However, medical image fusion is a resurgent field that needs to be enhanced to conquer the increasing challenges. This paper presents a comprehensive survey of some existing medical image fusion methods. It is expected that this study will be useful for the researchers scrutinizing medical image fusion. Furthermore, it is expected to establish a concrete foundation for developing more powerful fusion methods for medical applications.

The iris has been vastly recognized as one of the powerful biometrics in terms of recognition performance, both theoretically and empirically. However, traditional unprotected iris biometric recognition schemes are highly vulnerable to numerous privacy and security attacks. Several methods have been proposed to generate cancellable iris templates that can be used for recognition; however, these templates achieve lower accuracy of recognition in comparison to traditional unprotected iris templates. In this paper, a novel cancellable iris recognition scheme based on the salting approach is introduced. It depends on mixing the original binary iris code with a synthetic pattern using XOR operation. This scheme guarantees a high degree of privacy/security preservation without affecting the performance accuracy compared to the unprotected traditional iris recognition schemes. Comprehensive experiments on various iris image databases demonstrate similar accuracy to those of the original counterparts. Hence, robustness to several major privacy/security attacks is guaranteed.

This paper suggests novel two proposed cancellable biometric realization techniques recognition and template protection. In this paper, the Homomorphic Key (HK) encoding algorithm is utilized for cancelable face system. In the first suggested scheme, the HK algorithm is applied on the face images. The resultant map is encrypted, in order to the second HK utilized in the HK is produced from the image. This approach can be used to develop a frequency domain procedure for making this system for biometric template protection. The second algorithm presents a new approach to detect the features of face images depending the Speeded Up Robust Features (SURF) and Optimum Global Thresholding (OTSU) method. This algorithm is relied on scale space analysis with the number of SURF feature points as the key parameters for classification. Simulation results using evaluation metrics False Positive Rate (FPR), False Negative Rate (FNR), Equal Error Rate (EER), Receiver Operating Characteristic (ROC) and Area under ROC (AROC) prove that the first proposed cancelable biometric technique with the second key are best with comparing the other keys. The obtained results clear that the second suggested technique has sucesseded in detection the features of the skin, eyes, nose, hair, ears images and the face positions.

Speaker recognition revolution has lead to the inclusion of speaker recognition modules in several commercial products. Most published algorithms for speaker recognition focus on text-dependent speaker recognition. In contrast, text-independent speaker recognition is more advantageous as the client can talk freely to the system. In this paper, text-independent speaker recognition is considered in the presence of some degradation effects such as noise and reverberation. Mel-Frequency Cepstral Coefficients (MFCCs), spectrum and log-spectrum are used for feature extraction from the speech signals. These features are processed with the Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) as a classification tool to complete the speaker recognition task. The network learns to recognize the speakers efficiently in a text-independent manner, when the recording circumstances are the same. The recognition rate reaches 95.33% using MFCCs, while it is increased to 98.7% when using spectrum or log-spectrum. However, the system has some challenges to recognize speakers from different recording environments. Hence, different speech enhancement techniques, such as spectral subtraction and wavelet denoising, are used to improve the recognition performance to some extent. The proposed approach shows superiority, when compared to the algorithm of R. Togneri and D. Pullella (2011).

This paper investigates the effect of both decoding and decompression on the Speaker Identification (SI) in a remote access system. The coding and compression processes are used for the communication purpose as a normal action taken for voice communication over Internet or mobile networks. In the proposed system, the speech signal is coded with the Linear Predictive Coding (LPC) technique. Also, the speech signal is compressed using two techniques. The first technique depends on decimation process to compress the signal. The signal can be recovered using inverse solutions. The inverse solutions include maximum entropy and regularized reconstruction. The second technique is the Compressive Sensing (CS) and the speech signal can be reconstructed using linear programming. The coded or compressed speech signal is transmitted into the receiver via a wireless communication channel. At the receiver, the received signal is decoded or decompressed, and then SI is performed on the decoded or decompressed speech signal. The performance of coding and compression techniques is evaluated using some metrics such as Perceptual Evaluation of Speech Quality (PESQ) and Dynamic Time Warping (DTW). The objective of SI is to achieve the security needed for the remote access system, and this security can be increased using coding and compression processes. In the SI system, the feature vectors are captured from different discrete transforms such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST), besides the time domain. The recognition rate for all transforms is computed to evaluate the performance of the SI system.

Nowadays, optimization has become a brand methodology for different applications. One of the most promising fields for application of optimization is the image processing field, especially image fusion. A new effective deterministic optimization technique is the modified central force optimization (MCFO) that overcomes the low convergence rate drawback of the central force optimization (CFO). In this paper, the MCFO is applied with standard image fusion methods as a novel brand to improve the fusion efficiency either qualitatively or quantitatively. Intensity-hue-saturation (IHS), high-pass filtering (HPF), and discrete wavelet transform (DWT) are powerful standard techniques for satellite image fusion that are implemented with MCFO optimization in this paper. They are performed on satellite panchromatic (PAN) and multispectral (MS) images. The target of using the MCFO is to reduce some spectral and spatial distortions that may occur without optimization. Different qualitative indices have been used to validate the proposed approach comprising optimization for satellite image fusion.

This paper proposes a design of 2-D chaotic Baker map for image encryption which utilizes three modes of operations: 1) the cipher block chaining (CBC) mode, 2) the cipher feedback (CFB) mode, and 3) the output feedback (OFB) mode. The proposed image cryptosystem is characterized by a short encryption time of scalevariant images and a high level of confusion and diffusion due to its shuffling and substitution processes. This is useful in applications such as online streaming of paid videos, in which both the speed of encryption\decryption and a good encryption quality is required. A comparison between the proposed image cryptosystem, the traditional 2-D chaotic Baker map permutation cryptosystem, and the RC6 substitution cryptosystem is presented in the paper. A comparison is held with relevant techniques and the results reveal that the proposed image cryptosystem achieves a high degree of security. It is also more immune to noise than the RC6 cryptosystem and takes less processing time for images with large dimensions than both the chaotic cryptosystem and the RC6 cryptosystem. The superiority of the proposed cryptosystem has been proved for image encryption against the recent techniques from the cryptographic viewpoint.

In this era of technology, digital images turn out to be ubiquitous in a contemporary society and they can be generated and manipulated by a wide variety of hardware and software technologies. Copy-move forgery is considered as an image tampering technique that aims to generate manipulated tampered images by concealing unwanted objects or reproducing desirable objects within the same image. Therefore, image content authentication has become an essential demand. In this paper, an innovative design for automatic detection of copy-move forgery based on deep learning approaches is proposed. A Convolutional Neural Network (CNN) is specifically designed for Copy-Move Forgery Detection (CMFD). The CNN is exploited to learn hierarchical feature representations from input images, which are used for detecting the tampered and original images. The extensive experiments demonstrate that the proposed deep CMFD algorithm outperforms the traditional CMFD systems by a considerable margin on the three publicly accessible datasets: MICC-F220, MICC-F2000, and MICC-F600. Furthermore, the three datasets are incorporated and joined to the SATs-130 dataset to form new combinations of datasets. An accuracy of 100% has been achieved for the four datasets. This proves the robustness of the proposed algorithm against a diversity of known attacks. For better evaluation, comparative results are included.

Image forgery detection is the basic key to solve many problems, especially social problems such as those in Facebook, and court cases. The common form of image forgery is the copy-move forgery, in which a section of the image is copied and pasted in another location within the same image. In this type of image forgery, it is easy to perform forgery, but more difficult to detect it, because the features of the copied parts are similar to those of the other parts of the image. This paper presents an approach for copy-move forgery detection based on block processing and feature extraction from the transforms of the blocks. In addition, a Convolutional Neural Network (CNN) is used for forgery detection. The feature extraction is implemented with serial pairs of convolution and pooling layers, and then classification between the original and tampered images is performed with and without transforms. A comparison study between different trigonometric transforms in 1D and 2D is presented for detecting the tampered parts in the image. This comparison study is based on the completeness rate for the detection. This comparison ensures that the DFT in 1D or 2D implementations is the best choice to detect copy-move forgery compared to other trigonometric transforms. In addition, the paper presents a comparison study between ten cases using the CNN learning technique to detect the manipulated image. The basic idea is to use a CNN to detect and extract features. The proposed CNN approach can also be used for active forgery detection because of its robustness to detect the manipulation of digital watermarked images or images with signatures.

Biometric systems are widely used now for security applications. Two major problems are encountered in biometric systems: the security problem and the dependence on a single biometric for verification. The security problem arises from the utilization of the original biometrics in databases. So, if these databases are attacked, the biometrics are lost forever. Hence, there is a need to secure original biometrics by keeping them away from utilization in biometric databases. Cancelable biometrics is an emerging security trend in the field of biometric authentication. Cancelable biometric systems depend on the transformation of biometric features into new formats so that users can replace their biometric templates in the same or different systems. In this paper, we present a proposed cancelable face and fingerprint recognition algorithm based on the 3D jigsaw transform and optical encryption. The algorithm adopts the Fractional Fourier Transform (FRFT) in the optical encryption scheme with a single random phase mask. This structure can be implemented all optically with a single lens. The proposed cancelable biometric recognition algorithm employs an optical image encryption scheme that depends on two cascaded stages of 2D-FRFT with separable kernels in both dimensions. The two stages are separated with a random phase mask. A preceding bit plane permutation process is performed on the obtained biometrics prior to the FRFT operation to achieve a high level of security. To validate the proposed algorithm for cancelable biometric recognition, different sets of face and fingerprint images are used. A comparative study is presented between the proposed algorithm and the optical Double Random Phase Encoding (DRPE) algorithm. The simulations results obtained for performance evaluation show that the proposed algorithm is safe, reliable, and feasible. It has good encryption and cancelability that reveal good performance.

With the rapid growth of information and communication technologies, the number of security threats in computer networks is substantially increasing; thus, the development of more proactive security warning measures is required. In this work, we propose a new anomaly detection method that operates by decomposing TCP traffic into control and data planes, which exhibit similar behaviors in the absence of attacks. The proposed method exploits the statistics of the cross-correlation function of the two planes traffic and the constant false alarm rate (CFAR) scheme for detecting anomalies of the underlying network traffic. Both the fixed and adaptive thresholding schemes are implemented. The adaptive thresholding is setup by adjusting the value of the threshold in accordance with the local statistics of the cross-correlation function of the two planes traffic. We evaluate the performance of the proposed method by analyzing the real traffic captured from a deployed network and traffic obtained from other publicly available datasets; we focus on TCP traffic with three different aggregated count features: packet count, IP address count, and port count sequences. Although both the fixed and adaptive thresholding schemes perform well and detect the presence of a distributed denial-of-service efficiently. The adaptive thresholding scheme is more reliable because it detects anomalies as they start.

This paper aims to reduce the embedding errors, maintain the image fidelity, and reduce the errors, when detecting the embedded messages in images. An embedding approach is proposed that depends on using the orthogonal matrices of the Singular Value Decomposition (SVD) as a vessel for embedding information instead of embedding in the singular values of the images. Three ways are suggested to reduce the embedding errors and maintain the image fidelity, when detecting the embedded message. These ways are increasing the number of columns protected without embedding, choosing the suitable block size to embed in and adjusting the singular values in order to give a high quality of the stego image. Results show that utilization of the orthogonal matrices of the SVD for information hiding can be as effective as using transform-based techniques, and it gives better results than those obtained with the Least Significant Bit (LSB) technique.

This paper presents a proposed approach for the enhancement of Infrared (IR) night vision images. This approach is based on a trilateral contrast enhancement in which the IR night vision images pass through three stages: segmentation, enhancement and sharpening. In the first stage, the IR image is divided into segments based on thresholding. The second stage, which is the heart of the enhancement approach, depends on additive wavelet transform (AWT) to decompose the image into an approximation and details. Homomorphic enhancement is performed on the detail components, while plateau histogram equalization is performed on the approximation plane. Then, the image is reconstructed and subjected to a post-processing high-pass filter. Average gradient, Sobel edge magnitude and spectral entropy are used as quality metrics for evaluation of the proposed approach. The used metrics ensure good success of this proposed approach.

This paper presents a super-resolution (SR) technique for enhancement of infrared (IR) images. The suggested technique relies on the image acquisition model, which benefits from the sparse representations of low-resolution (LR) and high-resolution (HR) patches of the IR images. It uses bicubic interpolation and minimum mean square error (MMSE) estimation in the prediction of the HR image with a scheme that can be interpreted as a feed-forward neural network. The suggested algorithm to overcome the problem of having only LR images due to hardware limitations is represented with a big data processing model. The performance of the suggested technique is compared with that of the standard regularized image interpolation technique as well as an adaptive block-by-block least-squares (LS) interpolation technique from the peak signal-to-noise ratio (PSNR) perspective. Numerical results reveal the superiority of the proposed SR technique.

This paper presents an audio watermarking technique based on singular value decomposition (SVD) and fractional Fourier transform (FRT). The basic idea of this technique is to implement SVD watermarking on the audio signals in the FRT domain due to its recommended degree of security resulting from using a rotation angle in addition to the frequency-domain transformation. The SVD has an invariance to changes in the signal after watermark embedding. Hence, the proposed technique has a large degree of security and resistance to attacks. This technique is based on embedding an image watermark in either the audio signal or a transformed version of this signal. Experimental results show that watermark embedding in the FRT of an audio signal achieves less distortion of the audio signal in the absence of attacks. In the presence of attacks, it is recommended that the embedding is performed in the FRT of the audio signal to maintain a high detection correlation coefficient between the original watermark and the obtained watermark. A segment-based implementation of the proposed audio watermarking technique is also presented. This implementation succeeds in obtaining a high detection correlation coefficient in the presence of severe attacks. It is noticed from the results that in the presence of attacks, the SVD watermarking in the FRT domain with a phase angle of 5π/4 is better for watermark detection than watermarking using other angles in the FRT domain.

This paper presents a novel scheme for cancelable iris recognition based on comb filtering. This scheme begins with a coarse-to-fine iris localization stage. After that, Gabor filtering is applied for feature extraction. The two-dimensional phase pattern of features generated with the LogGabor filter is distorted through comb filtering. The objective of this distortion process is to generate a cancelable feature pattern that represents the iris. The ability to reinitiate a new cancelable pattern is guaranteed through the variation of the comb filter order. The proposed scheme is compared with a cancelable random projection scheme for iris recognition. Experimental results are conducted on CASIA-IrisV3-Interval database for both random projection and comb filtering schemes. Moreover, evaluation metrics are estimated for different comb filter orders of 6, 8, 10, and 12 in addition to the case of original iris features. Hamming distance and Receiver Operating Characteristic (ROC) curve are estimated for both random projection and comb filtering schemes to check robustness and stability. The experimental results show a significant gain in both privacy and performance. Also, the comb filtering scheme achieves a superior performance for all orders compared to the random projection scheme. The proposed comb filtering scheme achieves the highest accuracy of 99.75% for order 6 and a promising Equal Error Rate (EER) of 0.36% for order 10.

This paper presents an automatic inspection approach for Printed Circuit Boards (PCBs) with accurate determination of the fault location and identification of the fault type. This approach depends on several digital image processing techniques including registration, filtering, foreground segmentation, mathematical morphological operations, subtraction, feature extraction, and component matching. The Speeded Up Robust Feature extraction (SURF) technique is used for two purposes: registration of the PCB to be checked to a reference PCB and detection of feature points of each missing component on the PCB that is localized from the subtraction process from the reference PCB. Operation is performed on the hue component of the color PCB images. A dictionary is first built for all possible components on the available PCBs with SURF feature descriptors, and hence if a missing item is detected on a PCB during the inspection process, the SURF feature descriptors for features extracted from the difference between the tested and reference PCBs at the position of the lost component are matched with those in the built dictionary or database. A distance metric is used in the matching process. The importance of the proposed approach lies in its ability to build a dictionary of feature descriptors for all possible components in a diversity of PCBs and its ability to localize and identify the missing components regardless of the PCB position, rotation, or type. All operations are formulated in a Graphical User Interface (GUI) using MATLAB environment.

Color images usually have large storage sizes as they are composed of three planes in the raw image format represented with the red, green, and blue components. Traditional color image compression schemes can be used to save the storage size of the color images. Unfortunately, most of these schemes are lossy in nature, which affects the details of color images. This paper presents a different treatment to the color image storage problem depending on the original color image formation process. In the color image formation process, not all the red, green, and blue components of the color images are acquired, simultaneously. Only, one component at each pixel position is acquired and Color Filter Array (CFA) interpolation is used to estimate the other two components using interpolation algorithms like Minimized-Laplacian Residual Interpolation (MLRI) and Linear Interpolation with Laplacian Second Order Correction (LILSOC). We adopt a similar strategy in this paper for reducing the storage sizes of color images by 66.67% of their original sizes. The sensitivity of the pattern recognition process to the proposed color image storage and interpolation strategy is studied in this paper. A cepstral feature extraction algorithm is adopted in this paper for extracting features from the interpolated patterns for further classification. Moreover, two types of classifiers are considered and compared in this paper for the pattern recognition: Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs). Simulation results reveal the success of the proposed strategy for color image storage and interpolation in obtaining high-quality color images in addition to the high Recognition Rates (RR) of color patterns after interpolation. This success encourages the use of the proposed color image storage strategy in storing large volumes of color databases.

In the last years, most researches proved that spectrum holes are not efficiently utilized in wireless communications. Cognitive radio (CR) is an efficient solution to face inefficient utilization of spectrum resources. The key technique, which enables CR to provide efficient utilization of spectrum resources is called spectrum sensing. Spectrum sensing enables a secondary user (SU) to track the activity of the primary user (PU) and the availability of spectrum holes that can be used without any disturbance to the PU. Fixed sensing time schemes give inefficient throughput performance with varying received signal-to-noise ratios (SNRs). So, in this paper, an adaptive sensing time optimization scheme in cooperative CR based on energy detection is investigated with different fusion rules. The proposed scheme adapts the sensing time based on the value of received SNR to maximize the achieved throughput with an acceptable probability of false alarm. The performance of the proposed scheme is investigated with AND, OR, and Marjory fusion rules and compared to those of fixed sensing time schemes. Simulation results show that the proposed scheme significantly enhances the achieved throughput, and reduces the probability of false alarm compared to those of the fixed sensing time schemes. In addition, the proposed scheme provides better performance as the number of SUs increases with the marjory fusion rule.

Biometric systems are widely used now for security applications. Two major problems are encountered in biometric systems: the security problem and the dependence on a single biometric for verification. The security problem arises from the utilization of the original biometrics in databases. So, if these databases are attacked, the biometrics are lost forever. Hence, there is a need to secure original biometrics by keeping them away from utilization in biometric databases. Cancelable biometrics is an emerging security trend in the field of biometric authentication. The objective of cancelable biometrics is to generate fake versions of the biometrics through non-invertible transforms or encryption methods to save the original biometrics from being compromised to guarantee their security. The other problem of biometric verification is the dependence on a single biometric, which reduces the trustiness of the verification results. Hence, there is a bad need to use multiple biometrics for trusted verification results. Multiple biometrics can be acquired for the same person and used for verification with a majority voting scenario to ensure trusted verification results. So, there is a need to save all biometrics in a secure way, which allows authentication from each of them, afterwards. The storage of multiple biometrics consumes storage space. Hence, there is a need for some sort of compression to save this storage space, while keeping the discrimination ability of subjects. This paper presents a novel approach that solves the security, trustiness, and storage problems of biometric systems. It is a cancelable multi-biometric security system based on Double Random Phase Encoding (DRPE) and cepstral analysis. Four biometrics are comprised in a unified biometric template for each person using Discrete Cosine Transform (DCT) compression. This unified biometric template is encrypted with the DRPE algorithm for security purposes. The cancelability is guaranteed through the ability to change the random phase sequences of the DRPE algorithm if the database is compromised. The multi-biometric compression is performed through keeping the most significant coefficients in the DCT domain for all four biometrics. The biometric recognition is performed by decrypting the unified biometric template and applying a cepstral approach for verification of the subject. A majority voting scheme can be followed for biometric verification at the receivers of remote-access biometric systems. The main advantage of the proposed cancelable multi-biometric system is the large degree of security, the immunity to communication channel effects through the utilization of a majority voting strategy at the receiver, the ability to withstand the compression effect, and the irreversibility through the implementation of cepstral features for biometric verification.

Biometric recognition refers to the automated process of recognizing individuals using their biometric patterns. Recent advancements in deep learning and computer vision indicate that generic descriptors which are extracted using convolutional neural networks (CNNs) could represent complex image characteristics. This paper presents a number of cancelable fusion-based face recognition (FR) methods; region-based, multi-biometric and hybrid-features. The former included methods incorporate the use of CNNs to extract deep features (DFs). A fusion network combines the DFs to obtain a discriminative facial descriptor. Cancelabilitiy is provided using bioconvolving as an encryption method. In the region-based method, the DFs are extracted from different face regions. The multi-biometric method uses different biometric traits to train multiple CNNs. The hybrid-features method merges the merits of deep-learned features and hand-crafted features to obtain a more representative output. Also, an efficient CNN model is proposed. Experimental results on various datasets prove that; (a) the proposed CNN model achieves remarkable results compared to other state-of-the-art CNNs, (b) region-based method is superior to multi-biometric and hybrid-features methods and (c) the utilization of bio-convolving method increases the system security with a slight degradation in the recognition accuracy.

Automated abnormal brain discovery is an extremely crucial task for clinical diagnosis. Over a decade ago, various techniques had been displayed to improve this technology. This paper presents a hybrid system based on a combination of Gray Wolf Optimizer (GWO) and Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel to classify a given Magnetic Resonance (MR) brain image as benign or malignant. 5-fold cross validation was used to enhance generalization. We applied the hybrid system on 80 images (20 benign and 60 malignant), and found out that the classification accuracy was as high as 98.750%.

This paper presents a fingerprint image encryption scheme based on fingerprint image fusion with another visible image that is rich in details. The encryption process is performed with chaotic Baker map, which has large immunity to noise. The image fusion process is performed with the Haar wavelet transform, and it can be implemented with the average or maximum fusion rule. The fusion process is performed, because fingerprint images are not rich in details, and hence the direct application of chaotic Baker map encryption will not be efficient for encrypting this type of images. To obtain an image that is rich in details, it is possible to use another encrypted image with a strong ciphering algorithm such as the RC6. Several perspectives are considered for performance evaluation of the proposed encryption scheme including visual inspection, histogram analysis, correlation coefficient, entropy analysis, processing time, and the effect of noise after decryption. The proposed fingerprint encryption scheme is appropriate for cancelable biometric applications to preserve the privacy of users by keeping their original fingerprints away from usage in the recognition system. The simulation results demonstrate that the proposed image encryption scheme gives a proficient and secure path for unique encrypted fingerprints. Both Equal Error Rate (EER) and Area under Receiver Operating Characteristic (AROC) curve are used for performance evaluation of the proposed cancelable fingerprint recognition scheme revealing high performance.

Non-interactive biometric systems have gained an enormous interest from computer vision researchers as they provide more efficient and reliable ways of identification and authorization from a distance. Gait and face recognition are types of non-interactive biometric systems without users’ cooperation with the surveillance system. On the contrary to face recognition, gait recognition can manage low-resolution and low-brightness images. It aims to know the individuals based on their style and way of walking. Gait recognition has numerous applications in several domains, such as healthcare monitoring, security systems, and surveillance systems for indoor and outdoor activities. Yet, gait recognition performance is frequently deteriorated by some variety of factors, such as viewing angle variations, and clothing changes. Recently, deep learning models have been employed efficiently in gait recognition systems. They are more generic, since the feature construction process is completely automated. This paper presents gait features measured automatically in the midst of walking for the recognition system. To extract these features from a video of a moving object, two vital modules are used, namely the motion detection and tracking, and the feature extraction. Accordingly, the principal module serves to distinguish the walking style in an image sequence or video. A background subtraction technique is executed to fragment the movement of the background, and the moving area related to the spatial silhouette is correctly tracked and segmented. The second module “Feature Extraction” is used to extract the features from the sequence of silhouette images. The gait cycle is calculated from the shape changes of the silhouettes, and it is used to construct a small sequence of Gait Energy Images (GEI). The optical flow of the GEI is measured to extract only the moving parts and exclude the static ones. Finally, the Convolution Neural Network (CNN) is fed with the optical flow output to build unique features. These features are used for neural network training, and evaluation is performed on popular gait benchmark datasets. The obtained results reveal an accuracy level of 95% with more resistance to view and probe changes.

In the Three-Dimensional H.264 Multi-view Video Coding (3D H.264/MVC), the original 3D Video (3DV) sequence is a combination of variable video frames captured for the same object by different cameras. Therefore, in order to transmit 3DV content over limited-resources networks, a highly-efficient compression mechanism must be applied, while achieving a better reception quality. Moreover, in real-time applications such as 3DV conference and streaming, it is mandatory that the process of 3DV compression/decompression is speedy. Because it is known that most of the design complexity of the utilized 3D H.264/MVC codec come from the encoder part not from the decoder part, where the Motion Estimation (ME) process presents the highest computational complexity. In this work, an efficient implementation of the Enhanced Predictive Zonal Search (EPZS) ME algorithm is introduced for the 3D H.264/MVC standard. The EPZS algorithm is one of the most common and best ME algorithms. The overall inter-frame and inter-view prediction mechanisms including Motion Compensation (MC) and ME have been implemented. For validation and comparative analysis purposes, the outcomes of the suggested 3DV design for the EPZS ME algorithm are contrasted to more state-of-the-art ME algorithms. The suggested architecture of the EPZS ME algorithm is implemented in VHDL, synthesized utilizing Xilinx Virtex-6 FPGA and Xilinx ISE Design Suite 13.3, simulated employing ModelSim SE 6.5, and validated utilizing MATLAB SIMULINK. Experimental results prove that the suggested architecture achieves a low hardware complexity implementation and high-speed of 3D H.264/MVC compression process. This can be exploited for the utilization of the proposed work for real-time 3DV applications.

Digital image forgery detection is an important task in digital life as the image may be easily manipulated. This paper presents a novel blind tampering detection algorithm for images acquired from digital cameras and scanners. The algorithm is based on applying homomorphic image processing on each suspicious image to separate illumination from reflectance components. In natural images, it is known that the illumination component is approximately constant, while changes can be detected in tampered ones. Support Vector Machine (SVM) and Neural Network (NN) classifiers are used for classification of tampered images based on the illumination component, and their results are compared to obtain the best classifier performance. The Receiver Operating Characteristic (ROC) curve is used to depict the classifier performance. Three different color coordinate systems are tested with the proposed algorithm, and their results are compared to obtain the highest accuracy level. Joint Photographic Experts Group (JPEG) compressed images with different Quality Factors (QFs) are also tested with the proposed algorithm, and the performance of the proposed algorithm in the presence of noise is studied. The performance of the SVM classifier is better than that of the NN classifier as it is more accurate and faster. A 96.93% detection accuracy has been obtained regardless of the acquisition device.

In this paper, hybrid models for image quality enhancement are presented comprising both Contrast Limited Adaptive Histogram Equalization (CLAHE) and image interpolation. Adaptive histogram equalization is employed for contrast enhancement, while image interpolation is employed for resolution enhancement. Both the CLAHE and image interpolation are used interchangeably to check the most suitable model for quality enhancement of Low-Resolution (LR) images. The utilized interpolation techniques throughout this paper are polynomial and inverse techniques. Simulation results prove that the application of the CLAHE after interpolation gives the best image quality, especially with regularized inverse interpolation.

One of the known problems in security systems is to identify persons based on certain signatures. Biometrics have been adopted in security systems to identify persons based on some physiological or behavioral characteristics that they own. Photoplethysmography (PPG) is a physiological signal that is used to describe the volumetric change of blood flow in peripherals with heartbeats. The PPG signals gained some interest of researchers in the last few years, because they are used non-invasively, and they are easily captured by the emerging IoT sensors from fingertips. This paper presents a PPG-based approach to identify persons using a neural network classifier. Firstly, PPG signals are captured from a number of persons using IoT sensors. Then, unique features are extracted from captured PPG signals by estimating the Mel-Frequency Cepstral Coefficients (MFCCs). These features are fed into an Artificial Neural Network (ANN) to be trained first and used for identification of persons. A dataset of PPG signals for 35 healthy persons was collected to test the performance of the proposed approach. Experimental results demonstrate 100% and 98.07% accuracy levels using the hold-out method and the 10-fold cross-validation method, respectively.

The streaming of Three-Dimensional Video (3DV) over erroneous wireless channels causes Macro-Blocks (MBs) corruptions. Thus, the efficient performance of 3DV communication techniques over wireless channels has become an interesting research topic because of the restricted resources and the existence of severe communication losses. The 3DV consists of various video sequences captured via multiple cameras surrounding an object, and working simultaneously. Therefore, there is a need to achieve high encoding efficiency. Unfortunately, the highly-compressed 3DV content is subject to communication channel corruptions. Thus, in this research, we suggest the utilization of a chaotic randomization technique based on Baker map with convolution coding and equalization for high-quality 3D Multi-view Video Coding (MVC) and High Efficiency Video Coding (HEVC) transmission over an Orthogonal Frequency Division Multiplexing (OFDM) wireless system. Rayleigh fading and Additive White Gaussian Noise (AWGN) are considered in this paper in a real scenario of 3DV transmission. Firstly, the 3DV volume is compressed making use of the intra- and inter-prediction correlations between frames. Then, the compressed 3D-MVC and 3D-HEVC frames are converted into binary data format. After that, the chaotic randomization technique is employed before the modulation stage. It is utilized to minimize the channel corruptions on the streamed encoded 3DV data, and it as well introduces a degree of encryption to the transmitted 3DV frames. To evaluate the efficiency of the suggested hybrid framework; different simulations on several 3D-MVC and 3D-HEVC frames have been executed. The results prove that the delivered 3DV frames with the suggested framework have high Peak Signal-to-Noise Ratios (PSNRs).

Three-Dimensional Multi-View Video (3D-MVV) transmission over wireless networks suffers from losses. Therefore, the robust performance of 3D-MVV transmission techniques over wireless channels has become a recent hot research issue due to the restricted resources and the presence of severe channel errors. The 3D-MVV is composed of multiple video stream shots by several cameras around a single object, simultaneously. So, it is an urgent task to achieve high compression ratios to meet future bandwidth constraints. Unfortunately, the highly-compressed 3D-MVV data becomes more sensitive and vulnerable to packet losses, especially in the case of heavy channel errors. Thus, in this paper, we propose the application of a chaotic Baker map interleaving technique with equalization for efficient transmission of composite 3D-MVV compressed frames over an Orthogonal Frequency Division Multiplexing (OFDM) wireless channel. Rayleigh fading and Additive White Gaussian Noise (AWGN) are considered in the real scenario of 3D-MVV transmission. Firstly, the 3D-MVV content is compressed exploiting the intra- and inter-prediction correlations between frames. After that, a composite frame of luminance is generated from each four consecutive frames using Discrete Cosine Transform (DCT), which represents a second level of compression. The resultant composite frame is converted to binary data format. Then, chaotic interleaving is applied on the binary information prior to the modulation process. This chaotic interleaving is used to mitigate the OFDM induced Peak-to-Average Power Ratio (PAPR) problem and to reduce the wireless channel effects on the transmitted bit streams. It also adds a degree of encryption to the transmitted 3D-MVV compressed frames. To evaluate the performance of the proposed hybrid technique, several simulation experiments on different 3D-MVV frames have been executed. The experimental results show that the received 3D-MVV frames have high average Peak Signal-to-Noise Ratio (PSNR) gains up to 4.25 dB and a reduction of the average PAPR values by about 12 dB with the proposed hybrid technique compared to the other traditional techniques.

This paper presents a fabulous enhancement approach for infrared (IR) images. This approach mixes the benefits of the undecimated Additive Wavelet Transform (AWT) with the homomorphic transform and Contrast Limited Adaptive Histogram Equalization (CLAHE). The basic idea of this approach depends on applying the CLAHE on the IR image. Then, the resultant image is decomposed into sub-bands using the AWT. The homomorphic enhancement is implemented on each sub-band, separately, up to the sixth sub-band. The homomorphic enhancement is applied on the IR image in the log domain by decomposing the image into illumination and reflectance components. The illumination is attenuated, while the reflectance is magnified. Applying this method on each sub-band gives more details in the IR image. The performance quality metrics for the suggested approach are entropy, average gradient, contrast, and Sobel edge magnitude. Simulation results reveal the success of the proposed approach in enhancing the quality of IR images.

Error control techniques like Error Resilience (ER) and Error Concealment (EC) are preferred techniques to ameliorate the lost Macro-Blocks (MBs) in the 3D Video (3DV) communication systems. In this paper, we present different enhanced ER-EC algorithms for intra-frame images for 3DV and Depth (3DV + D) communication through wireless networks. At the encoder, the slice structured coding, explicit flexible macro-block ordering, and context adaptive variable length coding are utilized. At the decoder, a hybrid approach comprising spatial circular scan order interpolation algorithm and temporal partitioning motion compensation algorithm is suggested to reconstruct the Disparity Vectors (DVs) and Motion Vectors (MVs) of the erroneous color images. For the corrupted depth images, a depth-assisted EC algorithm is proposed. Then, the optimum concealment MVs and DVs are chosen by employing the weighted overlapping block motion and disparity compensation algorithm. Furthermore, the Bayesian Kalman Filter (BKF) is utilized as an amelioration tool due to its efficiency to smooth the remnant inherent corruptions in the formerly optimally chosen color and depth DVs and MVs to obtain a good video quality. Simulation results on several 3DV streams show that the suggested algorithms have extremely adequate subjective and objective video quality performance compared to the traditional methods, particularly at high Packet Loss Rates (PLRs).

A novel image encryption algorithm based on the Rubik’s cube scrambling is proposed in this paper to achieve simultaneous encryption of a group of images. This proposed encryption algorithm begins with chaotic Baker map permutation with a selected mode of operation or RC6 algorithm as a first step for encrypting the images, separately. After that, the obtained encrypted images are further encrypted in a second stage with Rubik’s cube. Chaotic or RC6 encrypted images are used as the faces of the Rubik’s cube. From the concepts of image encryption, the RC6 algorithm adds a degree of diffusion, while chaotic Baker map adds a degree of permutation. The Rubik’s cube algorithm adds more permutation to the encrypted images, simultaneously. The simulation results demonstrate that the proposed encryption algorithm is efficient, and it exhibits strong robustness and security. The encrypted images are further transmitted over a wireless channel with Orthogonal Frequency Division Multiplexing (OFDM) system, and decrypted at the receiver side. Evaluation of the quality of the decrypted images at the receiver side reveals good performance.

This paper presents two robust hybrid watermarking techniques for securing the Three-Dimensional High Efficiency Video Coding (3D-HEVC). The first watermarking technique is the homomorphic-transform-based Singular Value Decomposition (SVD) in Discrete Wavelet Transform (DWT) domain. The second watermarking technique is the three-level Discrete Stationary Wavelet Transform (DSWT) in Discrete Cosine Transform (DCT) domain. The objective of the two proposed hybrid watermarking techniques is to increase the immunity of the watermarked 3D-HEVC streams to attacks. Also, we propose a wavelet-based fusion technique to combine two depth watermark frames into one fused depth watermark frame. Then, the resultant fused depth watermark is encrypted using chaotic Baker map to increase the level of security. After that, the resultant chaotic encrypted fused depth watermark is embedded in the 3D-HEVC color frames using the proposed hybrid watermarking techniques to produce the watermarked 3D-HEVC streams. In addition to achieving multi-level security in the transmitted 3D-HEVC streams, the proposed hybrid techniques reduce the required bit rate for transmitting the color-plus-depth 3D-HEVC data over limited-bandwidth networks. The performance of the proposed hybrid techniques is compared with those of the state-of-the-art techniques. Extensive simulation results on standard 3D video sequences have been conducted in the presence of attacks. The obtained results confirm that the proposed hybrid fusion-encryption-watermarking techniques achieve not only a good perceptual quality with high Peak Signal-to-Noise Ratio (PSNR) values and less bit rate, but also high correlation coefficient values between the original and extracted watermarks in the presence of attacks. Furthermore, the proposed hybrid techniques improve the capacity of information embedding and the robustness without affecting the perceptual quality of the original 3D-HEVC frames. Indeed, the extraction of the encrypted, fused, primary, and secondary depth watermark frames is possible in the presence of attacks.

Digital watermarking is an efficient and promising mechanism for protecting the copyright of the transmitted multimedia information. Thus, this paper presents two robust hybrid color image watermarking techniques. The objective of the proposed watermarking techniques is to increase the immunity of the watermarked color images against attacks and to achieve adequate perceptual quality. The first proposed hybrid technique is the homomorphic transform based Singular Value Decomposition (SVD) in Discrete Wavelet Transform (DWT) domain. Firstly, the DWT is employed to divide an image into non-overlapping bands. Then, the reflectance components of the LL sub-bands are extracted using the homomorphic transform of each of the RGB (Red, Green, and Blue) color image components. After that, the watermark is embedded by applying the SVD on these reflectance components. The second proposed hybrid technique is the three-level Discrete Stationary Wavelet Transform (DSWT) in Discrete Cosine Transform (DCT) domain. In this technique, the RGB components of the host color image are separated, and then the DCT is applied on each separated color component. The three-level DSWT is employed to divide the DCT components into four sub-bands. These sub-bands are the A, H, V, and D matrices, which have the same host image size. The watermark image is then embedded into the determined matrix A. The two proposed hybrid watermarking techniques are compared with the current state-of-the-art techniques. This paper also presents a comparative study of the proposed techniques for different color imaging systems to determine their robustness and stability. The comparisons are based on the subjective visual results to detect any degradation in the watermarked image in addition to the objective results of the Peak Signal-to-Noise Ratio (PSNR) of the watermarked image, and the Normalized Correlation (NC) of the extracted watermark to test and evaluate the performance efficiency of the proposed watermarking techniques. Extensive experimental results show that the proposed hybrid watermarking techniques are both robust and have adequate immunity against different types of attacks compared to the traditional watermarking techniques. They achieve not only very good perceptual quality with appreciated PSNR values, but also high correlation coefficient values in the presence of different multimedia attacks.