Through the use of combined deep learning and anomaly detection approaches, this research investigates the area of cybersecurity threat detection. The study proves the framework's extraordinary success in recogniz...
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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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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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This paper shows how convolutional neural networks (CNNs) are useful for accurately and quickly diagnosing skin issues, underscoring the need of utilizing state-of-the-art machine learning approaches for accurate and ...
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Heart disease remains the leading cause of mortality globally, emphasizing the critical need for better diagnostics. This study used a variety of machine learning methods to forecast cardiac disease, using SVM, Random...
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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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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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Pediatric appendicitis, an acute inflammatory disease, arises from the obstruction of the appendix, often due to inflammation or a fecalith. This common abdominal emergency in children presents diagnostic challenges d...
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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.
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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