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3 articles for “Digital Forensic Challenges”
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Prospective Research Fields and Difficulties in Digital Forensic Evidence Analysis
Abstract: As technology becomes increasingly prevalent in modern life, digital gadgets are more likely to be relevant to a criminal investigation or civil case. Law enforcement organisations around the world are currently dealing with enormous backlogs of digital evidence due to the sheer volume of investigations requiring digital forensic expertise. Anticipated future trends suggest a significant rise in the volume of incidents necessitating digital forensic analysis. A growing variety of devices, …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 10, Issue 3, 2023 · pp. 24–28 Read article
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Evaluating Delay Impacts: From Root Cause Analysis to Dispute Resolution
Abstract: Construction projects inherently involve complex coordination among multiple stakeholders, interdependent tasks, and unpredictable external challenges, which frequently cause schedule overruns and disputes. Traditional contractual mechanisms—such as extensions of time (EoT), liquidated damages, and force majeure clauses—serve to define responsibilities, allocate risk, and establish formal dispute pathways when delays arise. For example, EoT provisions protect contractors from penalties when delays are beyond their control, provided proper notice and evidence are submitted …
Published in Journal of Structural Engineering and Management · Vol. 12, Issue 3, 2025 · pp. 11–25 Read article
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Enhancing Facial Recognition: Assessing CNNs for Detecting Image Manipulation
Abstract: Deepfake technology, powered by highly advanced deep learning models, has raised significant concerns regarding media manipulation, identity theft, and the spread of online disinformation. Due to the increasing sophistication of deepfake content, traditional forensic methods often fail to detect such artificially generated images with high accuracy. Consequently, deep learning-based approaches have become essential in combating this challenge. This study compares six prominent deep learning architectures: VGG16, ResNet50, MobileNetV2, InceptionV3, EfficientNetB0, …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 2, 2025 · pp. 27–36 Read article