Network traffic classification is a critical aspect of network management. Software-defined networking (SDN) technology offers a novel approach to network management by separating the control plane from the data plane...
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Image manipulation detection has gained significant attention due to the rise of Generative Models (GMs). Passive detection methods often overfit to specific GMs, limiting their effectiveness. Recently, proactive appr...
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Image manipulation detection has gained significant attention due to the rise of Generative Models (GMs). Passive detection methods often overfit to specific GMs, limiting their effectiveness. Recently, proactive approaches have been introduced to overcome this limitation. However, these methods suffer from two vulnerabilities: i) the manipulation detector is not robust to noise and hence can be easily fooled;ii) they rely on fixed perturbations for image protection, which offers an exploit for malicious attackers, enabling them to evade detection. To overcome these issues, we propose PADL, a novel solution that is able to create image-specific perturbations for protecting images. PADL’s key objective is to provide a secure and adaptive protection mechanism that ensures the authenticity of images by detecting and localizing manipulations, drastically reducing the possibility of reverse engineering. The method consists of two key components: an encoder, which conditions a learnable perturbation on the input image to ensure uniqueness and robustness against attacks, and a decoder, which extracts the perturbation and leverages it for manipulation detection and localization. PADL can detect manipulation of a protected image and pinpoint regions that have undergone alterations. Unlike previous proactive defenses that rely on a finite set of perturbations, PADL’s tailored protection significantly reduces the risk of reverse engineering. Although being trained only on images of faces manipulated with STGAN, PADL generalizes to a range of unseen models with diverse architectural designs, such as StarGANv2, CycleGAN, BlendGAN, DiffAE, StableDiffusion, and StableDiffusionXL and also to unseen data domains. Finally, we propose a novel evaluation protocol that fairly assesses localization performance in relation to detection accuracy, providing a better reflection of real-world scenarios. Future research will aim to extend PADL to work on more challenging scenarios, including v
Graph data represents information efficiently and can be used to learn subsequent tasks easily. In the domain of biological science, recommender systems, social network analysis graph representation learning has becom...
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This system provides a comprehensive overview of hospital environments by tracking air quality, dust, temperature, and humidity simultaneously, offering a more complete picture of indoor conditions than systems that f...
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Multilevel thresholding plays a crucial role in image processing, with extensive applications in object detection, machine vision, medical imaging, and traffic control systems. It entails the partitioning of an image ...
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Influence maximization (IM) is the task of selecting the most influential nodes in the network. IM achieves the goal of spreading information, influencing behaviour, or promoting sales of products. Existing studies in...
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Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome...
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Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.
Heart Disease (HD) significantly impacts global health, making its accurate prediction critical for reducing death rates. This paper proposes a novel optimization-based HD prediction system leveraging metaheuristic al...
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Cardiovascular diseases (CVD) are among the leading causes of mortality globally, posing significant challenges to healthcare systems. Therefore, this paper introduces a novel framework, Cardiovascular Disease Predict...
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In the contemporary digital era, spreading false information via social media and Internet platforms poses a significant challenge. Not only the data has become increasingly multimodal, lack of sufficient labeled data...
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