Browsing by Author "Kumara, P. M. I. N"

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Open Access
Developing an Optimal Strategy to Address the Vulnerability of Image Tampering
(SLIIT, 2024-12) Kumara, P. M. I. N
The paper proposes a hybrid image tampering detection system that incorporates the Convolutional Neural Networks into the pool of traditional forensic techniques such as Error Level Analysis and noise analysis. Its objective is to provide high detection accuracy in tampered images through deep learning and forensic methods. According to this method, ELA detects compression inconsistencies in the system, while noise analysis detects abnormal noise patterns in the image. A combination of these techniques provides the capability for the system to capture various methods for tampering, including copy-move forgery, splicing, and subtle retouching. It was trained and tested on the CASIA 2.0 dataset with high accuracy: 98% training accuracy and over 96% validation accuracy. It was successfully deployed as a real-time Flask web application wherein users can upload an image and perform the analysis very quickly. While powerful, the model has a limitation of only revealing a subset of lossless image format tampering and performs subtle manipulations. The future work will involve enhancing scalability and deepfake detection that can handle complex techniques of tampering. The research proposed herein provides a holistic and scalable solution for the detection of image tampering to be applied in digital forensics, verification of media, and cybersecurity
Embargo
Human and Organizational Threat Profiling Using Machine Learning
(IEEE, 2021-12-09) Kumara, P. M. I. N; Dananjaya, K. G. S; Amarasena, N. P. N. H; Pinto, H. M. S; Yapa, K; Rupasinghe, L
The usage of online social networking sites is increasing rapidly. But the downside is that the growth of various kinds of ongoing social media threats such as fake profiles, cyberbullying, and fake news. Many important observations can be made to increase the existing knowledge about social media threats by studying various information exchanged through public and organizations. One direction is to conduct studies on human behavior and personality traits using public user profile data and the organizational threat classifying. This research aims to build a system to predict human personality behaviors on social media profiles based on the OCEAN Model and company-based threat profiling. All the data collected relating to everyone in the consumer’s friend list is analyzed to obtain the threatening behaviors and classified according to the OCEAN to generate a threat report. Organizational network gathered log data for filtered log protection against malware. Logs received from these endpoints will be collected by collectors. Those logs will be forwarded to our filter, made of a Machine Learning Algorithm (MLA). This will be a custom MLA specially designed for this purpose. MLA will classify and categorize threats according to their severity, filtered log protection system against malware and other threats.