In the field of image forensics,image tampering detection is a critical and challenging *** methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits ...
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In the field of image forensics,image tampering detection is a critical and challenging *** methods based on manually designed feature extraction typically focus on a specific type of tampering operation,which limits their effectiveness in complex scenarios involving multiple forms of *** deep learningbasedmethods offer the advantage of automatic feature learning,current approaches still require further improvements in terms of detection accuracy and computational *** address these challenges,this study applies the UNet 3+model to image tampering detection and proposes a hybrid framework,referred to as DDT-Net(Deep Detail Tracking Network),which integrates deep learning with traditional detection *** contrast to traditional additive methods,this approach innovatively applies amultiplicative fusion technique during downsampling,effectively combining the deep learning feature maps at each layer with those generated by the Bayar noise *** design enables noise residual features to guide the learning of semantic features more precisely and efficiently,thus facilitating comprehensive feature-level ***,by leveraging the complementary strengths of deep networks in capturing large-scale semantic manipulations and traditional algorithms’proficiency in detecting fine-grained local traces,the method significantly enhances the accuracy and robustness of tampered region *** with other approaches,the proposed method achieves an F1 score improvement exceeding 30% on the DEFACTO and DIS25k *** addition,it has been extensively validated on other datasets,including CASIA and *** results demonstrate that this method achieves outstanding performance across various types of image tampering detection tasks.
Due to increased network traffic and security concerns, intrusion detection system (IDS) research has received a lot of attention in computer science. Advanced intrusion detection systems have arbitrary incursion cate...
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作者：
Gote, Pradnyawant M.Kumar, PraveenDhale, TrishulMishra, Gaurav Vedprakash
Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India
Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India
Jawaharlal Nehru Medical College Department of Radiodiagnosis Maharashtra Wardha442001 India
Disease diagnosis using non-invasive techniques is a key determinant in the improvement of outcomes in patients and the enhancement of precautionary health measures. The study focuses on the design and development of ...
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Semi-supervised object detection (SSOD) has emerged as a critical approach to bridge the gap between the extensive labeled data requirements of supervised methods and the abundance of unlabeled data available. However...
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In the age of universal computing,human life is becoming smarter owing to the recent developments in the Internet of Medical Things(IoMT),wearable sensors,and telecommunication innovations,which provide more effective...
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In the age of universal computing,human life is becoming smarter owing to the recent developments in the Internet of Medical Things(IoMT),wearable sensors,and telecommunication innovations,which provide more effective and smarter healthcare *** has the potential to shape the future of clinical research in the healthcare *** sensors,patients,healthcare providers,and caregivers can connect through an IoMT network using software,information,and communication *** assisted living(AAL)allows the incorporation of emerging innovations into the routine life events of *** learning(ML)teaches machines to learn from human experiences and to use computer algorithms to“learn”information directly instead of relying on a *** the sample size accessible for learning increases,the performance of the algorithms *** paper proposes a novel IoMT-enabled smart healthcare framework for AAL to monitor the physical actions of patients using a convolutional neural network(CNN)algorithm for fast analysis,improved decision-making,and enhanced treatment *** simulation results showed that the prediction accuracy of the proposed framework is higher than those of previously published approaches.
The aim of this paper is to analyze the implementation of intelligent lighting within the concept of smart energy based on the possibility of saving and efficient use of energy, which is largely based on non-renewable...
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In this paper, an age of information (AoI)-aware joint design framework of sampling, transmission, computation, and control is considered for industrial cyber-physical systems. To enhance the control performance, we i...
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Advanced technology such as microrobots and nanorobots have the potential to completely transform the healthcare industry. These tiny robotic devices provide fine control for a range of biological applications since t...
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Magnesium chips were coated with a high concentration of graphite using a binder and were used as the raw material for injection molding. The microstructure of the magnesium injection-molded product with added graphit...
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The home delivery service is a crucial part of the delivery process. Still, it has the highest financial and trustworthiness costs because it's the only part that directly touches the customer. If it works, the cu...
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