Knife handling is one of the most important skills in cooking. However, it is difficult to practice the handling of a kitchen knife repeatedly owing to limitations in resources. To address this, we propose a highly re...
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Stream ciphers are essential cryptographic primitives widely employed in various applications for efficient data protection. This paper presents a comprehensive review of attacks on the Grain, Trivium, and SNOW stream...
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The complexity of contemporary communication further emphasizes the need to automate monotonous work to increase efficiency and effectiveness. This paper introduces a new advance, voice-controlled Automail AI, in the ...
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Transparency in a Supply chain is the availability of relevant information as it relates to all participants of the Supply Chain. Every stakeholder in the Agricultural and Pharmaceutical Supply Chain specifically the ...
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Cyberbullying has grown to be a significant issue in the internet age, affecting individuals of all ages, particularly teens. It involves using internet channels to harass, threaten, or disparage people, often with li...
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The paper presents a dual-model approach that can be used for categorizing the work experience data from resumes or other textual copra. The application tracking system (ATS), which is the main tool used to screen res...
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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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We extract propositional conditional belief bases from multilayer perceptrons, a basic type of feedforward neural networks, and investigate the relation between these two prevalent formalisms from knowledge representa...
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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.
Although recent supercomputers have been improving their computational performance, achieving performance scaling with respect to the number of nodes is not easy due to long inter-node communication latency. Many atte...
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