Split learning is a neural network training approach that can overcome the limitations of traditional deep neural networks in edge artificial intelligence environments. It offers the advantage of privacy protection be...
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Split learning is a neural network training approach that can overcome the limitations of traditional deep neural networks in edge artificial intelligence environments. It offers the advantage of privacy protection because it transmits intermediate features that are calculated via the client-side model and the client does not need to send the original input data to the server. However, concerns remain regarding data privacy leakage because an attacker can still attempt model inversion attacks based on the intermediate features. We introduce several shortcomings of existing defense techniques for such attacks and present a new defense approach called TrapMI. The proposed method can induce an attacker to generate a class-specific target image that appears different from the original image when inverting the input image. We analyze the performance through quantitative and qualitative evaluations. Furthermore, the AutoGenerator is proposed to overcome the problem whereby the client cannot perform modulation that requires the target image because the class of the input image is unknown during this phase. De-identified images are automatically modulated in the inference phase using this approach. The proposed method was evaluated on two datasets, three classification models, and three split points. Its resistance was measured using a deeper and stronger inverse model than those in previous studies. Overall, the proposed method ensures data privacy protection at a significantly higher level while maintaining a similar task performance to that of existing defense technologies.
With the rise of multi-modal large language models, accurately extracting and understanding textual information from video content-referred to as video-based optical character recognition (Video OCR)-has become a cruc...
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History of code elements is essential for software maintenance tasks. However, code refactoring is one of the main causes that makes obtaining a consistent view on code evolution difficult as renaming or moving source...
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Many real-world data can be modeled as heterogeneous graphs that contain multiple types of nodes and edges. Meanwhile, due to excellent performance, heterogeneous graph neural networks (GNNs) have received more and mo...
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This paper investigates the performance of Mobile Edge Computing (MEC) systems using the M/M/m queuing model, focusing on how server configurations and queuing rules affect task response times on edge servers. The stu...
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The Winograd Schema Challenge (WSC) is a popular benchmark for commonsense reasoning. Each WSC instance has a component that corresponds to the mention of the correct answer option of the two options in the context. W...
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Poisson's equation is one of the most popular partial differential equation (PDE), which is widely used in image processing, computer graphics and other fields. However, solving a large-scale Poisson's equatio...
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UAV (Unmanned Aerial Vehicle) navigation can be considered as the process of robots that determine how to successfully and quickly reach the target location. Specifically, in order to complete the scheduled task succe...
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There are two key distinctions between cloud and on-premise (OP) software, the cost for each varies and so does the level of control. As organisations explore to reduce costs, many data and rules are migrating to mult...
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Script is the structured knowledge representation of prototypical real-life event *** the commonsense knowledge inside the script can be helpful for machines in understanding natural language and drawing commonsensibl...
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Script is the structured knowledge representation of prototypical real-life event *** the commonsense knowledge inside the script can be helpful for machines in understanding natural language and drawing commonsensible *** learning is an interesting and promising research direction,in which a trained script learning system can process narrative texts to capture script knowledge and draw ***,there are currently no survey articles on script learning,so we are providing this comprehensive survey to deeply investigate the standard framework and the major research topics on script *** research field contains three main topics:event representations,script learning models,and evaluation *** each topic,we systematically summarize and categorize the existing script learning systems,and carefully analyze and compare the advantages and disadvantages of the representative *** also discuss the current state of the research and possible future directions.
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