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Wireless Mesh Networks (MR-WMNs) are characterized by having multiple radio interfaces with which, node can send/receive data to/from multiple nodes. This feature increases the network capacity and improves data delivery rate. However, interference between the co-located channels is one of the main constraints that hinder such networks from achieving the optimal utilization of the available resources such as bandwidth and transmission routes. Bottleneck is one of the consequences of such limitation, which leads to network fragmentation and performance degradation especially in the critical links shared by multiple paths connecting the nodes with the gateway. Although several studies have been conducted to address such limitation, they were all built on the premise of stationary topology, which does not hold for real world WMNs that include mobile users in addition to backbone nodes and routers. To this end, this paper proposes a user mobility-aware channel assignment algorithm based on weighted link ranking to fairly allocate the channels and minimizes the interference, thus improves the capacity of the network. Multiple criteria were used to rank those links before applying the channel assignment algorithm. Those criteria were obtained from traffic and network topology such as distance from the gateways, interference index, and traffic load. The results from numerical simulation demonstrate that the proposed scheme has reduced the interference which, consequently, improved the network capacity.

Texture analysis is devised to address the weakness of color-based image segmentation models by considering the statistical and spatial relations among the group of neighbor pixels in the image instead of relying on color information of individual pixels solely. Due to decent performance of the gray-level co-occurrence matrix (GLCM) in texture analysis of natural objects, this study employs this technique to analyze the human skin texture characteristics. The main goal of this study is to investigate the impact of major GLCM parameters including quantization level, displacement magnitudes, displacement direction and GLCM features on skin segmentation and classification performance. Each of these parameters has been assessed and optimized using an exhaustive supervised search from a fairly large initial feature space. Three supervised classifiers including Random Forest, Support Vector Machine and Multilayer Perceptron have been employed to evaluate the performance of the feature space subsets. Evaluation results using Edith Cowan University (ECU) dataset showed that the proposed texture-assisted skin detection model outperformed pixelwise skin detection by significant margin. The proposed method generates an F-score of 91.98, which is satisfactory, considering the challenging scenario in ECU dataset. Comparison of the proposed texture-assisted skin detection model with some state-of-the-art skin detection models indicates high accuracy and F-score of the proposed model. The findings of this study can be used in various disciplines, such as face recognition, skin disorder and lesion recognition, and nudity detection.

Color space conversion utilized by many researchers in order to enhance skin recognition performance by projecting the skin color cluster to a more distinctive distribution. In spite of the substantial research effort in this area, finding a suitable color space for face and skin recognition is still an unsolved issue. Deviation of skin tone under different lighting condition, dissimilarity of skin color among different ethnics and races, various camera sensors characteristics, presence of skin-like color objects in image background and variation of skin color tone among different body limbs are among the major challenges in skin recognition. Majority of these challenges are expected to be mitigated through color space conversion. This paper proposes a new hybrid color space by applying Principal Component Analysis technique to skin color cluster in ten existing conventional color spaces including RGB, YCbCr , YUV, nRGB, i1i2i3, YIQ, XYZ, YPbPr , YES, YCgCr . The proposed hybrid color space which termed P1P2P3 consist of the three major Principal Components of these conventional color spaces components. Using Algebraic simplification these principal component has been reformulated in terms of RGB color space. Parametric pixel wised skin detection techniques have been employed in order to evaluate the proposed color space effect on skin detection performance. Three popular supervised classifiers including Multilayer Perceptron, Support Vector Machine and Random Forest has been employed to generate a parametric model of skin color cluster using the proposed color space. Experiment results shows the proposed hybrid color space P1P2P3 with F -score and False Positive Rate 0.960 and 0.041 respectively performed better than the existing conventional color spaces in term of pixel wised skin recognition.

Ethnicity identification for demographic information has been studied for soft biometric analysis, and it is essential for human identification and verification. Ethnicity identification remains popular and receives attention in a recent year especially in automatic demographic information. Unfortunately, ethnicity identification technique using color-based feature mostly failed to determine the ethnicity classes accurately due to low properties of features in color-based. Thus, this paper purposely analyses the accuracy of the color-based ethnicity identification model from various color spaces. The proposed model involved several phases such as skin color feature extraction, feature selection, and classification. In the feature extraction process, a dynamic skin color detection is adapted to extract the skin color information from the face candidate. The multi-color feature was formed from the descriptive statistical model. Feature selection technique applied to reduce the feature space dimensionality. Finally, the proposed ethnicity identification was tested using several classification algorithms. From the experimental result, we achieved a better result in multi-color feature compared to individual color space model under Random Forest algorithm.

Ethnicity identification for demographic information has been studied for soft biometric analysis, and it is essential for human identification and verification. Ethnicity identification remains popular and receives attention in a recent year especially in automatic demographic information. Unfortunately, ethnicity identification in a multi-class which consist of several ethnic classes may degrade the accuracy of the ethnic identification. Thus, this paper purposely analyses the accuracy of the texture-based ethnicity identification model from facial components under four-class ethnics. The proposed model involved several phases such as face detection, feature selection, and classification. The detected face then exploited by three proposed face block which are 1×1, 1×2 and 2×2. In the feature extraction process, a Grey Level Co-occurrence Matrix (GLCM) under different face blocks were employed. Then, final stage was undergone with several classification algorithms such as Naïve Bayes, BayesNet, k-Nearest Neighbour (k-NN), Random Forest, and Multilayer Perceptron (MLP). From the experimental result, we achieved a better result 2×2 face block feature compared to 1×1 and 2×2 feature representation under Random Forest algorithm.

Copy-move forgery detection (CMFD) has become a popular an important research focus in digital image forensic. Copy-move forgery happens when a region in an image is copied and paste into the same image. Apart from the main problem of detection robustness and accuracy, CMFD is struggle with time complexity issue. One of the options to resolve this problem was by including pre-processing step in CMFD pipeline. This paper reviews on the importance of pre-processing step, and available techniques in reducing time complexity of copy-move forgery detection. An experiment using discrete wavelet transform (DWT) as a pre-processing technique was carried out to evaluate the performance of adopting pre-processing technique in CMFD pipeline. The experimental result has shown a significant reduction in processing time with some trade off to detection accuracy.

Wireless Mesh Networks (WMNs) are promising means to provide inexpensive deployment, flexible and fast broadband access. Recent WMNs use multiple-radio and multiple channels to provide high performance. However, interference between channels is considered the key challenge for WMN performance. In WMN, the data flows are directed from/to the gateways. Thus, the quality of the critical links close to the gateways should be properly considered during channel assignments. Otherwise, network fragmentation and bottleneck problem may occur which affect the performance of the network due to congestions, and unfair channel distribution. Unfortunately, the existing channel assignments focusing only on the links close to the gateways and neglecting other critical links. This paper proposes a fair channel assignment algorithm based on weighted link ranking scheme in order to minimize the interference and thus improve the capacity of the network. The links are fairly ranked based on multiple criteria obtained from traffic and network topology such as distance from the gateways, interference index, and, traffic load. The results from numerical simulation demonstrate that the proposed channel assignment algorithm has reduced the interference, improving the network capacity, and achieves the fairness of channel distribution.

The presence of motion artefacts in ECG signals can cause misleading interpretation of cardiovascular status. Recently, reducing the motion artefact from ECG signal has gained the interest of many researchers. Due to the overlapping nature of the motion artefact with the ECG signal, it is difficult to reduce motion artefact without distorting the original ECG signal. However, the application of an adaptive noise canceler has shown that it is effective in reducing motion artefacts if the appropriate noise reference that is correlated with the noise in the ECG signal is available. Unfortunately, the noise reference is not always correlated with motion artefact. Consequently, filtering with such a noise reference may lead to contaminating the ECG signal. In this paper, a two-stage filtering motion artefact reduction algorithm is proposed. In the algorithm, two methods are proposed, each of which works in one stage. The weighted adaptive noise filtering method (WAF) is proposed for the first stage. The acceleration derivative is used as motion artefact reference and the Pearson correlation coefficient between acceleration and ECG signal is used as a weighting factor. In the second stage, a recursive Hampel filter-based estimation method (RHFBE) is proposed for estimating the ECG signal segments, based on the spatial correlation of the ECG segment component that is obtained from successive ECG signals. Real-World dataset is used to evaluate the effectiveness of the proposed methods compared to the conventional adaptive filter. The results show a promising enhancement in terms of reducing motion artefacts from the ECG signals recorded by a cost-effective single lead ECG sensor during several activities of different subjects.

Electrocardiogram (ECG) is beneficial to diagnose various heart diseases. Recently, mobile ECG has received the attention of researchers and practitioners for real time monitoring and automatic diagnosis. However, the performance of ECG’s monitoring system is degraded because of many disturbances created by subject movement, which leads to wrong diagnosis. Several attempts have been performed to remove the noise from clinical ECG signal using various digital signal processing techniques. Those techniques are not directly appropriate to be used for the mobile ECG environment and expected noises. Ultimately, motion artifact still is an open issue in mobile ECG. In this paper, an algorithm is proposed to reduce motion artifact using three sequential adaptive noise filters based on least mean square errors and reference noise estimation methods. The proposed algorithm is evaluated using real dataset that was collected while the subject performing different activities. Results show promising enhancement in the ECG signal quality.

The Underwater Sensor Network (UWSN) is main interesting area due to its most valuable applications like: disaster preventions, distributed tactical surveillance, undersea exploration, seismic monitoring, environmental monitoring and many more. The design of energy efficient routing protocol however is a challenging issue because in underwater environment the batteries of the sensor nodes cannot be recharged easily. Majority of the researchers have adapted the terrestrial WSN methodologies to overcome this problem but in underwater environment the terrestrial WSN approach is not feasible due to the acoustic signaling and water current. This research paper focuses the key limitation of the current energy efficient routing protocols. The simulation results with comparative analysis for energy efficient routing protocols are also presented in this research article; which helps the researchers to find the further research gap in the field of energy efficient routing protocols.

Apart from robustness and accuracy of copy-paste image forgery detection, time complexity also plays an important role to evaluate the performance of the system. In this paper, the focus point is to improve time complexity of the block-matching algorithm. Hence, a coarse-to-fine approach is applied to propose an enhanced duplicated region detection model by using sequential block clustering. Clustering minimizes the search space in block matching. This significantly improves time complexity as it eliminates several extra block-comparing operations. We determine time complexity function of the proposed algorithm to measure the performance. The experimental results and mathematical analysis demonstrate that our proposed algorithm has more improvement in time complexity when the block size is small.

Electrocardiogram (ECG) is a powerful tool which allows for diagnosing heart condition. Nowadays, wearable ECG recording devices are used in continuous monitoring and to provide health related information. However, these systems suffer from motion artifacts which remains an unsolved problem. In this paper, two wavelet-based techniques are presented and applied for ECG denoising with an evaluation of their performances. These methods are: wavelet shrinkage denoisingand multi-resolution thresholding using stationary wavelet transformation (SWT). An improved multi-resolution thresholding technique is proposed. This technique combines between the two former methods. Benchmark datasets and simulated noises were used to evaluate thedenoising techniques. The results shows that the current methods still cannot cope with motions artifacts, even the proposed technique improves only the smoothness of the ECG signal.

Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969., {"references":["Kovac, J., P. Peer, and F. Solina, Human Skin Color Clustering for Face Detection, in EUROCON 2003. Computer as a Tool. The IEEE Region 8. 2003, IEEE. p. 144 - 148.","Sobottka, K. and I. Pitas, A novel method for automatic face segmentation, facial feature extraction and tracking. Signal Processing: Image Communication, 1998. 12(3): p. 263-281.","Zaidan, A.A., et al., An Automated Anti-Pornography System Using A Skin Detector Based on Artificial Intelligence: A Review. International Journal of Pattern Recognition and Artificial Intelligence, 2013. 27(04): p. 1350012.","Abadpour, A. and S. Kasaei, Pixel-Based Skin Detection for Pornography Filtering. Iranian Journal of Electrical & Electronic Engineering, 2005. 1(3): p. 21-41.","Elgammal, A., C. Muang, and D. Hu, Skin Detection - a Short Tutorial, in Encyclopedia of Biometrics, S. Li and A. Jain, Editors. 2009, Springer US. p. 1218-1224.","Abdullah-Al-Wadud, M., S. Mohammad, and C. Oksam, A skin detection approach based on color distance map. EURASIP Journal on Advances in Signal Processing, 2009. 2008: p. 1-10.","Kakumanu, P., S. Makrogiannis, and N. Bourbakis, A survey of skin-color modeling and detection methods. Pattern Recognition, 2007. 40(3): p. 1106-1122.","Kawulok, M., J. Nalepa, and J. Kawulok, Skin Detection and Segmentation in Color Images, in Advances in Low-Level Color Image Processing, M.E. Celebi and B. Smolka, Editors. 2014, Springer Netherlands. p. 329-366.","Gonzales, R. and E. Woods, Digital Image Processing. 2002, New Jersey: Prentice Hall.\n[10]\tChen, W., et al., Skin color modeling for face detection and segmentation: a review and a new approach. Multimedia Tools and Applications, 2014: p. 1-24.\n[11]\tBhoyar, K. and O. Kakde, Skin Color Detection Model Using Neural Networks and its Performance Evaluation 1. 2010.\n[12]\tAl-Mohair, H.K., J. Mohamad-Saleh, and S.A. Suandi, Human Skin Color Detection: A Review on Neural Network Perspective. International Journal of Innovative Computing, Information and Control (ICIC), 2012. 8(12): p. 8115-8131.\n[13]\tAbdullah-Al-Wadud, M. and C. Oksam. Skin Segmentation Using Color Distance Map and Water-Flow Property. in Information Assurance and Security, 2008. ISIAS '08. Fourth International Conference on. 2008.\n[14]\tAl-Mohair, H.K., et al., Skin detection in luminace images using threshold technique. International Journal of Computer, the Internet and Management, 2007. 15(1): p. 25.\n[15]\tHasan, M.M. and P.K. Mishra, Superior Skin Color Model using Multiple of Gaussian Mixture Model. British Journal of Science, 2012. 6(1): p. 1-14.\n[16]\tPhung, S.L., D. Chai, and A. Bouzerdoum. A universal and robust human skin color model using neural networks. in IJCNN'01. International Joint Conference on Neural Networks. Proceedings. 2001. IEEE.\n[17]\tLee, J.Y. and S.I. Yoo. An Elliptical Boundary Model for Skin Color Detection. in Proceeding of the International Conference on Imaging Science, Systems and Technology. 2002.\n[18]\tAl-Mohair, H.K., J. Mohamad Saleh, and S.A. Suandi, Hybrid Human Skin Detection Using Neural Network and K-Means Clustering Technique. Applied Soft Computing, 2015. 33: p. 337-347.\n[19]\tDoukim, C.A., et al., Combining neural networks for skin detection. arXiv preprint arXiv:1101.0384, 2011.\n[20]\tTaqa, A.Y. and H.A. Jalab, Increasing the Reliability of Fuzzy Inference System-based Skin Detector. American Journal of Applied Science, 2010. 7(8): p. 1129-1139.\n[21]\tAbadpour, A. and S. Kasaei. Comprehensive Evaluation of the Pixel-Based Skin Detection Approach for Pornography Filtering in the Internet Resources. in Int. Symposium on Telecommunications, Shiraz, Iran. 2005.\n[22]\tOghaz, M.M., et al., A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique. PloS one, 2015. 10(8): p. e0134828."]}

Underwater Wireless Sensor Networks (UWSNs) is interesting area for researchers.To extract the information from seabed to water surface the the majority numbers of routing protocols has been introduced. The design of routing protocols faces many challenges like deployment of sensor nodes, controlling of node mobility, development of efficient route for data forwarding, prolong the battery power of the sensor nodes, and removal of void nodes from active data forwarding paths. This research article focuses the design of the Reliable Multipath Energy Efficient Routing (RMEER) which develops the efficient route between sensor nodes, and prolongs the battery life of the nodes. RMEER is a scalable and robust protocol which utilizes the powerful fixed courier nodes in order to enhance the network throughput, data delivery ratio, network lifetime and reduces the end-to-end delay. RMEER is also an energy efficient routing protocol for saving the energy level of the nodes. We have used the NS2.30 simulator with AquaSim package for performance analysis of RMEER.We observed that the simulation performance of RMEER is better than D-DBR protocol.

This paper proposes a Multi-Level Sampling (MLS) approach for continuous Loss of Self-Similarity (LoSS) detection using iterative window. The method defines LoSS based on Second Order Self-Similarity (SOSS) statistical model. The Optimization Method (OM) is used to estimate self-similarity parameter since it is fast and more accurate in comparison with other estimation methods known in the literature. Probability of LoSS detection is introduced to measure continuous LoSS detection performance. The proposed method has been tested with real Internet traffic simulation dataset. The results demonstrate that normal traces have probability of LoSS detection below the threshold at all sampling levels. Meanwhile, false positive detection can occur where abnormal traces have probability of LoSS that imitates normal behavior at sampling levels below 100ms. However, the LoSS probability exceeds the threshold at sampling levels larger than 100ms. Our results show the possibility of detecting anomaly traffic behavior based on obtaining continuous LoSS detection monitoring

The Underwater Sensor Network (UWSN) is main interesting area due to its most valuable applications like: disaster preventions, distributed tactical surveillance, undersea exploration, seismic monitoring, environmental monitoring and many more. The design of energy efficient routing protocol is a challenging issue because in underwater environment the batteries of the sensor nodes cannot be recharged easily. Majority of the researchers have adapted the terrestrial WSN methodologies to overcome this problem but in underwater environment the terrestrial WSN approach is not feasible due to the acoustic signaling and water current. This research paper focuses the key limitation of the energy efficient routing protocols. The simulation results with comparative analysis for energy efficient routing protocols are also presented in this research article; which helps the researchers to find the further research gap in the field of energy efficient routing protocols.

Underwater Wireless Sensor Network (UWSN) can enable many scientific, military, safety, commercial and environmental applications. Majority of the network models has been introduced for the deployment of sensor nodes through routing schemes and methodologies along with different algorithms but still the design of routing protocol for underwater environment is a challenging issue due to distinctive characteristics of underwater medium. The majority of the issues are also needed to fulfill the appropriate approach for the underwater medium like limited bandwidth, high bit error rates, propagation delay, and 3D deployment. This paper focuses the comparative analysis of the localization based routing protocols for UWSN. This comparative analysis plays a significant attention to construct a reliable routing protocol, which provides the effectual discovery of the route between the source node and the sink node. In addition this comparative analysis also focuses the data packets forwarding mechanism, the deployment of sensor nodes and location based routing for UWSN in different conditions.

Image block matching is the main step of duplicated region detection for exploring copy-paste image forgery. High computational time in this step is one of the most important problems to find similar regions. In this paper we propose an efficient image block matching algorithm based on two layer feature extraction in order to improve time complexity. Furthermore, we determine performance of proposed algorithm based on time complexity function. The experimental results and mathematical analysis demonstrate that two layer matching can be more time-efficient than previous common methods such as lexicographically sorting.

This paper analyzes loss of self-similarity (LoSS) detection accuracy using parameterpsilas adjustment which includes different values of sampling level and correlation lag. This is important when considering exact and asymptotic self-similar models concurrently in the self-similarity parameter estimation method. Due to the needs of high accuracy and fast estimation, the optimization method (OM) based on second order self-similarity (SOSS) statistical model was proposed in the previous works to estimate self-similarity parameter. Consequently, curve fitting error (CFE) value estimated from OM is used to detect LoSS efficiently. This work investigates the effect of the parameterpsilas adjustment for improving the CFE accuracy and estimation time speed. We have tested the method with real Internet traffics simulation that consists of normal and malicious packets traffic. Our simulation results show that LoSS detection accuracy and estimation time can be affected by the chosen of sampling level and correlation lag values.

This paper proposes a continuous Loss of Self-Similarity (LoSS) detection using iterative window and Multi-Level Sampling (MLS) approach. The method defines LoSS based on Second Order Self-Similarity (SOSS) statistical model. The Optimization Method (OM) is used to estimate self-similarity parameter since it is fast and more accurate in comparison with other estimation methods known in the literature. The probability of LoSS detection is introduced to measure continuous LoSS detection performance. The proposed method has been tested with real Internet traffic simulation dataset. The results demonstrate that normal traces have probability of LoSS detection below the threshold at all sampling levels. Meanwhile, abnormal traces have probability of LoSS that imitates normal behavior at sampling levels below 100 ms but exceeds the threshold at sampling levels larger than 100 ms. Our results show the possibility of detecting anomaly traffic behavior based on obtaining continuous LoSS detection monitoring.

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications.

Duplicated region detection is a technical method to expose copy-paste forgeries on digital images. Copy-paste is one of the common types of forgeries to clone portion of an image in order to conceal or duplicate special object. In this type of forgery detection, extracting robust block feature and also high time complexity of matching step are two main open problems. This paper concentrates on computational time and proposes a local block matching algorithm based on block clustering to enhance time complexity. Time complexity of the proposed algorithm is formulated and effects of two parameter, block size and number of cluster, on efficiency of this algorithm are considered. The experimental results and mathematical analysis demonstrate this algorithm is more costeffective than lexicographically algorithms in time complexity issue when the image is complex., {"references":["J. Fridrich, \"Detection of copy-move forgery in digital images,\" in\nProceedings of Digital Forensic Research Workshop, 2003, pp. -.","Popescu and H. Farid, \"Exposing digital forgeries by detecting duplicated\nimage regions,\" Tech. Rep., 2004.","W. Luo, \"Robust detection of region-duplication forgery in digital image,\"\nin IEEE Computer Society Washington, DC, USA, vol. Proceedings\nof the 18th International Conference on Pattern Recognition - Volume\n04, 2006, pp. 746 - 749-.","Lukas, Fridrich, and Goljan, \"Detecting digital image forgeries using\nsensor pattern noise,\" in Security, Steganography, and Watermarking of\nMultimedia Contents VIII, vol. 6072, San Jose, CA, 2006, pp. -.","B. Mahdian, \"Using noise inconsistencies for blind image forensics,\"\nvol. 27, pp. 1497-1503-, 2009.","M. K. Johnson and H. Farid, \"Exposing digital forgeries in complex\nlighting environments,\" vol. 2, pp. 450-461-, 2007.","Popescu and H. Farid, \"Exposing digital forgeries in color filter array\ninterpolated images,\" vol. 53, pp. 3948-3959-, 2005.","A. Popescu and H. Farid, \"Exposing digital forgeries by detecting traces\nof re-sampling,\" Tech. Rep., 2003.","S. Bayram, \"An efficient and robust method for detecting copy-move\nforgery,\" in IEEE International Conference on Acoustics, Speech and\nSignal Processing, vol. Washington, DC, USA, 2009, pp. -.\n[10] B. Mahdian and S. Saic, \"Detection of copy-move forgery using a\nmethod based on blur moment invariants,\" vol. 171, pp. 180-189-, 2007.\n[11] Z. Zhang, \"A survey on passive-blind image forgery by doctor method\ndetection,\" in Proceedings of the Seventh International Conference on\nMachine Learning and Cybernetics. Kunming: IEEE, 2008, pp. -.\n[12] B. Mahdian, \"A bibliography on blind methods for identifying image\nforgery,\" vol. Signal Processing: Image Communication, pp. 389399-,\n2010.\n[13] H. J. Lin, C. W. Wang, and Y. T. Kao, \"Fast copy-move forgery\ndetection,\" vol. 5, pp. 188-197-, 2009.\n[14] T. Sergois and Koutroumbas, Pattern Recognition - book - Third Edition.\nSan diego: Elsevier, 2006.\n[15] Stirling, \"Stirling approximation for n!\" pp. -."]}