Volume-10 , Issue-4 , Apr - 2022 Go Back

Image steganography is used as a covert communication technique which hides secret data in cover image intelligently so that it is visually imperceptible. This is often used by individual or organization with bad intent to harm people, organization or society. Steganalysis technique is used to break these systems to extract the secret information, reveal such covert communication and thwart imminent threat. Steganalytic techniques can be broadly classified as targeted or blind. In the former the knowledge of steganographic system used should be known and the latter adopts a more general approach where no knowledge of the process used to hide data is required. This paper studies some well-established statistical methods of targeted steganalysis and gray level co-occurrence matrix based blind steganalysis and compare their performances.

RS, Sample pair analysis, Chi-squared, Gray level co-occurrence matrix

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Bibek Ranjan Ghosh, "A Comparative Study of LSB based Statistical Steganalysis and Gray Level Co-Occurrence Matrix based Blind Image Steganalysis," International Journal of Computer Sciences and Engineering, Vol.10, Issue.4, pp.1-5, 2022.

Human bite mark analysis is most demanding and complicated part of forensic dentistry, involving identification of assailant by comparing record of their dentition with record of bite mark left on a victim. Bite marks are unique to individual such as distance and angles between teeth, missing, and teeth fillings. This type of impression evidence can be left in the skin of a victim. Following the identification of an injury as a bite mark, the comparison of the pattern produced to a suspect’s dentition is very vital. This article contains the current methods of comparison of human bite marks using different methods and technologies.

Bite marks, Forensic images, comparison overlays, Suspect identification, Bite mark analysis

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M. Chandramouleeswaran, N. Puviarasan, "A Review on Comparison of Human Bite Marks in Forensic Images," International Journal of Computer Sciences and Engineering, Vol.10, Issue.4, pp.6-10, 2022.

Breast cancer has become the most common cause of death in women. Early detection of breast cancer helps out to reduce the risk factors. Three classification algorithms (NB, DT, and KNN) were used on two different Breast cancer datasets using the WEKA tool. The main purpose of this paper is to compare the results of the classification algorithms using voting and feature selection methods. The experimental result shows that voting of three classifiers gives the highest performance accuracy on the Breast cancer dataset. The ensemble method is used to increase the accuracy of the data mining algorithms. We also compare the performance accuracy of classifiers using feature selection methods (IG and PCA) on breast cancer datasets.

J48,NaïveBayes,KNN,Voting classifier, feature selection

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Jyoti Negi, K.L. Bansal, "Feature Selection and Ensemble Method Analysis for Breast Cancer Datasets," International Journal of Computer Sciences and Engineering, Vol.10, Issue.4, pp.11-15, 2022.

Fraudulent behaviour are suspicious activities that usually occur before a crime takes place. These suspicious activities are being carried out on a day-to-day basis in banks, supermarkets, restaurants, Bus stop, offices, residential buildings, companies e.t.c. Within the banking industry, fraudulent behaviour is when a customer or a person makes suspicious moves before committing a crime. In this paper, an online predictive system for mapping visual scene against fraudulent behaviour was developed. The dataset for this system was collected from Kaggle database. The analysis of the video clips gave a total of 427 frames, 380 was visually mapped to be of fraudulent behaviour while 47 was being mapped to be of normal behaviour. These frames were used in training a convolutional neural network for detecting fraudulent behaviour from a video clip. The proposed model was deployed to web using python and flask framework. Our result gave about 99.99%. The proposed system was compared with that of Nakib et.al. (2018). The result of Nakib et.al. (2018) gave an accuracy of about 90.2% while that of our proposed system gave 99.99%.

Fraudulent Behaviour, Visual Scenes, Surveillance Camera, Deep Learning

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C. Ubani, V.I.E. Anireh, N.D. Nwiabu, "An Online Predictive System for Mapping Visual Scenes of Fraudulent Behaviour," International Journal of Computer Sciences and Engineering, Vol.10, Issue.4, pp.16-20, 2022.