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    A Dual-Branch CNN and Metadata Analysis Approach for Robust Image Tampering Detection
    (Institute of Electrical and Electronics Engineers Inc., 2025) Zakey, A; Bawantha, D; Shehara, D; Hasara, N; Abeywardena, K.Y; Fernando, H
    Image tampering has become a widespread issue due to the availability of advanced tools such as Photoshop, GIMP, and AI-powered technologies like Generative Adversarial Networks (GANs). These advancements have made it easier to create deceptive images, undermining their reliability and fueling misinformation. To address this growing problem, we propose a hybrid approach for image forgery detection, combining deep learning with traditional forensic techniques. Our study integrates a dual-branch Convolutional Neural Network (CNN) with handcrafted features derived from Error Level Analysis (ELA), noise residuals from the Spatial Rich Model, and metadata analysis to enhance detection capabilities. Metadata analysis plays a crucial role in identifying inconsistencies in image properties such as timestamps, geotags, and camera details, which often accompany tampered images. The CASIA dataset, a publicly available benchmark for tampered images, was used to train and evaluate the proposed model. After 30 epochs of training, the hybrid method achieved an accuracy of 95%, demonstrating its effectiveness in distinguishing between authentic and tampered images. This research highlights the advantages of combining deep learning models with traditional feature extraction methods and metadata analysis, offering a robust solution for detecting manipulated images. Our findings contribute to advancing image forensics by improving detection accuracy, even in cases involving sophisticated tampering methods driven by AI.
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    Stealth Eye: Behavioral Analysis for Fileless Malware Detection
    (Institute of Electrical and Electronics Engineers Inc., 2025) Bandara H.M.H.M; Ayeshani K.M.N; Kumari M.M.P.M; Wijerathna D.M.S.T; Abeywardena, K.Y; Wijesooriya, A
    Fileless malware is a significant cybersecurity threat as it is entirely present in system memory and evades traditional signature-based detection methods. This paper introduces STEALTH EYE, an endpoint behavioral analysis framework for detecting fileless malware, such as ransomware, spyware, trojans, and RedLine Stealer, in real time. The framework utilizes an endpoint agent that monitors system activity in real time and captures snapshots of behavior every 60 seconds for real- time threat analysis. These captures track memory injections, DLL loading and execution, file and handle operations, service activity, process and thread behavior, registry modifications, network communications, cryptographic function usage, keystroke logging, and clipboard access. The data that is collected is analyzed through supervised machine learning mechanisms to detect patterns that indicate fileless malware activity. In contrast to traditional post-infection forensic approaches, STEALTH EYE provides real-time monitoring, notification, and active response with enhanced cybersecurity resilience against the widespread fileless attacks.