Insights into Manipulation: Unveiling Tampered Images Using Modified ELA, Deep Learning, and Explainable AI

Digital image forgery (DIF) is a prevalent issue in the modern age, where malicious actors manipulate images for various purposes, including deception and misinformation. Detecting such forgeries is a critical task for maintaining the integrity of digital content. This thesis explores the use of Modified Error Level Analysis (ELA) in combination with a Convolutional Neural Network (CNN), as well as, Feedforward Neural Network (FNN) model to detect digital image forgeries. Additionally, incorporation of Explainable Artificial Intelligence (XAI) to this research provided insights into the process of decision-making by the models. The study trains and tests the models on the CASIA2 dataset, emphasizing both authentic and forged images. The CNN model is trained and evaluated, and Explainable AI (SHapley Additive exPlanation— SHAP) is incorporated to explain the model’s predictions. Similarly, the FNN model is trained and evaluated, and XAI (SHAP) is incorporated to explain the model’s predictions. The results obtained from the analysis reveals that the proposed approach using CNN model is most effective in detecting image forgeries and provides valuable explanations for decision interpretability.

Data Classification Using Combination of Five Machine Learning Techniques

Data clustering plays a vital role in object identification. In real life we mainly use the concept in biometric identification and object detection. In this paper we use Fuzzy Weighted Rules, Fuzzy Inference System (FIS), Fuzzy C-Mean clustering (FCM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) to distinguish three types of Iris data called Iris-Setosa, Iris-Versicolor and Iris-Virginica. Each class in the data table is identified by four-dimensional vector, where vectors are used as the input variable called: Sepal Length (SL), Sepal Width (SW), Petal Length (PL) and Petal Width (PW). The combination of five machine learning methods provides above 98% accuracy of class identification.

Traffic Modelling of Low Dense Femtocellular Network for Long Term Evolution

One of the features of Long Term Evolution (LTE) is the deployment of femtocells as the underlain cell of a macrocell without any intervention of frequency planning to offload the traffic. In this paper we used Markov chain to derive the expression of blocking probability for both macro and femtocell in terms of traffic parameters of the network. We developed an analytical model to find the expression of probability of forced termination (FT) using combination of mobility model and probability tree considering low dense femtocellular network. Two different trees were designed: a newly originating call which starts its session in a femtocell and that of in a macro cell. The link parameters of small scale fading of wireless network under Multiple Input Multiple Output (MIMO) are combined with the proposed traffic model to get the probability of FT of a real-life network. A new state transition chain was also developed including its solution for LTE traffic of variable bandwidth (BW) and a comparison was made with Erlang’s traffic model.

Determination of Array Gain of Single Hop to Achieve the Performance of a 2-Hop Wireless Link

In adaptive beamforming system adaptive algorithm of digital filter is applied to update the weighting vector of the antenna elements to get antenna gain along the desired direction and attenuation along the jammer. The objective of the paper is to evaluate the threshold gain of the adaptive beam former along the line of sight (LOS) between the transmitter and the receiver (including jammer suppression) to make the single hop link comparable with 2-hop link. The single hop and 2-hop communication systems are compared in context of symbol error rate (SER) under fading condition theoretically and verified by simulation. Finally we evaluate the numerical value of threshold gain of adaptive beamformer of two antenna elements under Rayleigh and Nakagami-m fading conditions.

Performance Evaluation of Multiple Classifiers for Predicting Fake News

The rise of fake news on social media has had a detrimental effect on society. Numerous performance evaluations on classifiers that can detect fake news have previously been undertaken by researchers in this area. To assess their performance, we used 14 different classifiers in this study. Secondly, we looked at how soft voting and hard voting classifiers performed in a mixture of distinct individual classifiers. Finally, heuristics are used to create 9 models of stacking classifiers. The F1 score, prediction, recall, and accuracy have all been used to assess performance. Models 6 and 7 achieved the best accuracy of 96.13 while having a larger computational complexity. For benchmarking purposes, other individual classifiers are also tested.

Towards the Advancement of Cashless Transaction: A Security Analysis of Electronic Payment Systems

In recent decades, day-to-day lifestyle requires online payments as easy and simple solutions to several financial transactions, which makes the concept of Electronic payment Systems very popular in the growth of a cashless society. In fact, cashless transactions through simple mobile apps are not merely a concept anymore rather are implemented robustly and being used extensively. On the dark side, obvious financial benefits are making these apps vulnerable to being attacked, which can be successful through security breaches. These cybersecurity issues need to be traced out and resolved to make the financial transactions through an app secure and trustworthy. In this paper, several related papers are analyzed to trace out possible cybersecurity issues in the domain of Electronic Transaction System. The objective is to establish sufficient theoretical background to propose methodologies for measuring security issues and also identify the security strength of any FinTech application and provide standard security metrics.