International Journal of Forensic Software Engineering (5 papers in press)

Regular Issues

• A hybrid approach to find cloned objects in copy move forged images  
  by Ashish Kumar Chakraverti, Vijay Dhir 
  Abstract: In this paper, we have proposed a new hybrid approach to detecting the cloned object in copy move forged image. Presently images are a very powerful medium to communicate the messages across the world wide web. Social media is used to spread the image as the message in the way of the nuclear reaction. With the help of highly efficient and advanced image editing tools and techniques, images are modified intentionally and then spread over the world wide web. Due to the availability of these advance and efficient image editing tools, the genuineness of images is now highly questionable. There are so many types of image forgery, one of the very popular image forgeries is the copy-move forgery. In this forgery, images are forged by making the clone of objects of the same image with the help of copy past method. There are so many techniques to detect similar objects in the forged image. In our proposed work we have integrated two states of art techniques named Adhoc method and Principle Component Analysis (PCA) based Scale Invariant Feature Transform (SIFT) method and created a new hybrid technique to detect the cloned object in the forged image. Our technique is implemented in the two- step. In the first step, we have preprocessed the object image by the proposed Dynamic Block Size Technique based Local Contrast Modification and Contrast Limited Adaptive Histogram Equalization (DBST-LCM CLAHE) up to desired Peak Signal to Noise Ratio (PSNR) and grey level. After that in the second step, we have integrated Adhoc, and PCA based SIFT algorithm to design a hybrid approach. We have tested our proposed method over CoMoFoD image database. Our proposed method shows the better performance in comparison of the state of art techniques regarding false positive rate(FPR), true positive rate(TPR), and PSNR.
  Keywords: Image Processing; Image Enhancement; Adhoc; PSNR; Imagernforgery; SIFT; PCA; CLAHE; Noise.
  
  
• Design of Framework for Ontological Component Retrieval from Software Component Repositories  
  by Iqbaldeep Kaur 
  Abstract: Modern component retrieval approaches are based on the context and domain of the software component in addition to using various classification and retrieval techniques like keyword search, attribute based search and semantic search. This work proposes Ontological Retrieval framework for software components that makes the use of ontology for effective retrieval of components. Ontological Component Retrieval framework has been created to facilitate the users and developers to retrieve software components matching precisely to their needs. A well-framed set of queries ranging from little to specific knowledge about the component requirement has been used to validate the results
  Keywords: Ontology; Software Component; Context Search; Semantic web services; Retrieval; Novelty Ratio; Lucene-Indexed Search; Reusability.
  
  
• Performing Opinion Mining and Analytical Study for Cashless Transactions  
  by Sonakshi Vij, Amita Jain, Devendra Tayal 
  Abstract: Cashless economy and digital payments are one of the hot topics of research that are gaining enormous popularity day by day. It is primarily due to their ability to combat issues of corruption, back money, illegal transactions and counterfeit money. With the increase in number of options of digital payments, people are inclining themselves towards ease of payment. This paper focuses on two aspects: firstly it provides an analytical study performed in the field of cashless transactions and digital payments. Secondly, it finds out the opinion of the people about cashless transactions using a hybrid approach of type 2 fuzzy logic and hesitant fuzzy sets for polarity assignment. A sentiment analysis on Cashless Economy has been carried out using data obtained from Twitter in the form of Tweets.
  Keywords: Cashless Economy; Digital Payments; Hesitant Fuzzy Sets; Opinion Mining; Sentiment Analysis; Type 2 Fuzzy Logic.
  
  
• State Estimation based Target Tracking and Applications of Multi Sensor Data Fusion  
  by David Kondru, Mehmet Celenk, Xiaoping Shen 
  Abstract: Detection, discrimination and tracking of a target under a given dynamic environment is one of the main challenges of an integrated sensory system. A combination of two or more sensors will always provide a better position estimate rather than a single sensor. The advantages of multi-sensor data fusion over a conventional single sensor tracking are presented in this paper. Using state estimators from simple linear rustic filters to a complex nonlinear filter, the tracking of target performing three different motions with sensor noises are presented in this paper. RADAR and the infrared search and track (IRST) are the two sensors considered based on which a complete mathematical modelling and simulation of the sensor measurements and tracking methodologies are utilised. The extension of the paper also presents the image fusion techniques using a widely known technique known as principle component analysis method. The RMS errors in position as well as image error measurements are performed that shows the superiority of the multi sensor data fusion process.
  Keywords: Kalman filter; Particle filter; measurement fusion; state vector fusion; Image fusion; Principle Component Analysis.
  DOI: 10.1504/IJFSE.2019.10021151
   
  
• Data Preprocessing Based On Missing Value and Discretization  
  by Neeta Yadav, Neelendra Badal 
  Abstract: In the real world, data is not available in the appropriate form for mining or extracting information from this. Generally, real-world data is incomplete, inconsistent and dirty so it is very necessary to process data smartly according to the requirement of the dataset. Preprocessing is one of the most crucial steps in data mining and most of the time spent in this about 60% of the time. Unprocessed data takes lots of time in mining. End-user wasted lots of time in getting the desired result. So it is very necessary to process data according to the specific dataset by applying techniques of processing and thereby it reduces the overall mining time, the end user gets the desired result more fastly. In this paper preprocessing of missing value and discretization has been done. Preprocessing of missing value handle by three techniques that is a deletion, Replacement by mean or averages, and prediction method. From these three techniques user opt the best technique for handling missing value, which gives maximum accuracy and takes less time for preprocessing. After handling the missing value, discretization is done for data reduction so it minimizes the preprocessing time.
  Keywords: Deletion; Discretization; Missing value; Prediction; Replacement by Mean.