Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things...
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Vision is a crucial aspect for both artificial intelligence and automated robots. In the case of an automated coconut harvesting machine, a computerized system linked to the machine plays a key role in real-time ident...
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Digital image forgery is the process of manipulating an image to deceive or mislead observers with real or manipulated content. Median filtering is widely used to smooth images and obscure traces of tampering, making ...
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ISBN:
(纸本)9791188428137
Digital image forgery is the process of manipulating an image to deceive or mislead observers with real or manipulated content. Median filtering is widely used to smooth images and obscure traces of tampering, making its detection critical for image forensics. However, identifying median filtering becomes more complex when additional operations, such as compression, resampling, or noise addition, are applied. To address this issue, we propose a lightweight convolutional neural network (CNN) model named SobelMNet, specifically designed for detecting median filtering in compressed images. The proposed model utilises a Sobel filter-based preprocessing step to enhance the residual differences between the original and manipulated images. These residuals, which capture subtle features indicative of median filtering, are analysed by CNN for classification. Further, the proposed model is evaluated on grayscale low-resolution images generated from the Dresden dataset for both binary and multiclass classification tasks. The model achieved a remarkable detection accuracy of 99.43% in median filter detection and outperformed state-of-the-art methods in various scenarios, including combinations of median filtering with Gaussian blur, resampling, and additive white Gaussian noise (AWGN) with an average accuracy of 98.32%. Finally, its lightweight architecture ensures computational efficiency, making it practical for real-world forensic applications. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.
Recognition of human activities has drawn a lot of interest lately in the field of computer vision and machine learning. Group activity recognition is a significant subcategory in which several people participate in a...
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As of right now, lung cancer is the primary cause of cancer-related deaths worldwide for both men and women. One possible explanation for lung cancer's main cause is smoking. 86% to 96% of instances of lung cancer...
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Considering the advancements in autonomous driving technologies, the necessity for an advanced driver assistance system (ADAS) to incorporate a multitude of sensors for enhanced precision has become paramount. Consequ...
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With the growing interest of researchers in the area of cloud computing, security emerges as a major area of concern. As the complexity of cloud environments grows and the dependency on cloud services deepens across v...
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The ubiquitous use of data in a plethora of use cases and different organizational dependence on it for various insight has made data a valuable resource across business and scientific domains. Data has attained much ...
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All industrial machine learning (ML) projects have their ultimate objective to quickly develop and deploy ML solutions. However, a lot of machine learning projects are failing, and never reach production. In order to ...
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This paper presents a novel Maximize Bandwidth utilization and Multipath Routing for Congestion Control (MBMRCC) scheme designed to address congestion issues in MANETs effectively. The MBMRCC scheme integrates multipa...
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