Enhanced Manhattan-based Clustering using Fuzzy C-Means Algorithm for High Dimensional Datasets

The problem of mining a high dimensional data includes a high computational cost, a high dimensional dataset composed of thousands of attribute and or instances. The efficiency of an algorithm, specifically, its speed is oftentimes sacrificed when this kind of dataset is supplied to the algorithm. Fuzzy C-Means algorithm is one which suffers from this problem. This clustering algorithm requires high computational resources as it processes whether low or high dimensional data. Netflix data rating, small round blue cell tumors (SRBCTs) and Colon Cancer (52,308, and 2,000 of attributes and 1500, 83 and 62 of instances respectively) dataset were identified as a high dimensional dataset. As such, the Manhattan distance measure employing the trigonometric function was used to enhance the fuzzy c-means algorithm. Results show an increase on the efficiency of processing large amount of data using the Netflix ,Colon cancer and SRCBT an (39,296, 38,952 and 85,774 milliseconds to complete the different clusters, respectively) average of 54,674 milliseconds while Manhattan distance measure took an average of (36,858, 36,501 and 82,86 milliseconds, respectively)  52,703 milliseconds for the entire dataset to cluster. On the other hand, the enhanced Manhattan distance measure took (33,216, 32,368 and 81,125 milliseconds, respectively) 48,903 seconds on clustering the datasets. Given the said result, the enhanced Manhattan distance measure is 11% more efficient compared to Euclidean distance measure and 7% more efficient than the Manhattan distance measure respectively

Dynamic S-Box and PWLCM-Based Robust Watermarking Scheme

Due to the increased number of cyberattacks, numerous researchers are motivated towards the design of such schemes that can hide digital information in a signal. Watermarking is one of the promising technologies that can protect digital information. However, traditional watermarking schemes are either slow or less secure. In this paper, a dynamic S-Box based efficient watermarking scheme is presented. The original image was extracted at the receiver’s end without any loss of sensitive information. Firstly, the Secure Hash Algorithm is applied to the original image for the generation of the initial condition. Piece Wise Linear Chaotic Map is then used to generate 16 × 16 dynamic Substitution Box (S-Box). As an additional security feature, the watermark is substituted through dynamic S-Box. Hence, it is hard for the eavesdroppers to attack the proposed scheme due to the dynamic nature of S-Box. Lastly, lifting wavelet transform is applied to the host image and the High Low and High High blocks of host image are replaced with least significant bits and most significant bits of the substituted watermark, respectively. Robustness, efficiency and security of the proposed scheme is verified using Structure Similarity Index, Structure Dissimilarity Index, Structure Content, Mutual Information, energy, entropy, correlation tests and classical attacks analysis

Tatouage robuste d’images imprimées

Invisible watermarking for ID images printed on plastic card support is a challenging problem that interests the industrial world. In this study, we developed a watermarking algorithm robust to various attacks present in this case. These attacks are mainly related to the print/scan process on the plastic support and the degradations that an ID card can encounter along its lifetime. The watermarking scheme operates in the Fourier domain as this transform has invariance properties against global geometrical transformations. A preventive method consists of pre-processing the host image before the embedding process that reduces the variance of the embeddable vector. A curative method comprises two counterattacks dealing with blurring and color variations. For a false alarm probability of 10⁻⁴, we obtained an average improvement of 22% over the reference method when only preventative method is used. The combination of the preventive and curative methods leads to a detection rate greater than 99%. The detection algorithm takes less than 1 second for a 512×512 image with a conventional computer, which is compatible with the industrial application in question.Le tatouage invisible d’images d’identité imprimées sur un support en plastique est un problème difficile qui intéresse le monde industriel. Dans cette étude, nous avons développé un algorithme de tatouage robuste aux diverses attaques présentes dans ce cas. Ces attaques sont liées aux processus d’impression/numérisation sur le support plastique ainsi qu’aux dégradations qu’une carte plastique peut rencontrer le long de sa durée de vie. La méthode de tatouage opère dans le domaine de Fourier car cette transformée présente des propriétés d’invariances aux attaques géométriques globales. Une méthode préventive consiste en un prétraitement de l’image originale avant le processus d’insertion qui réduit la variance du vecteur support de la marque. Une méthode corrective comporte deux contre-attaques corrigeant le flou et les variations colorimétriques. Pour une probabilité de fausse alarme de 10⁻⁴, nous avons obtenu une amélioration moyenne de 22% par rapport à la méthode de référence lorsque seule la méthode préventive est utilisée. La combinaison de la méthode préventive avec la méthode corrective correspond à un taux de détection supérieur à 99%. L’algorithme de détection prends moins de 1 seconde pour à une image de 512×512 pixels avec un ordinateur classique ce qui est compatible avec l’application industrielle visée