Titlebook: Information Security Applications; 24th International C Howon Kim,Jonghee Youn Conference proceedings 2024 The Editor(s) (if applicable) an

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A Blockchain-Based Mobile Crowdsensing and Its Incentive Mechanismtworthy mobile users often contribute lower-quality data and attempt to manipulate reward distributions unfairly. These problems will indirectly make mobile users more passively participate in the sensing task. To address these challenges, we propose a novel MCS system model that combines the public

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A New Frontier in Digital Security: Verification for NFT Image Using Deep Learning-Based ConvNeXt Mocial challenge. This paper proposes a verification framework for NFT images in a quantum blockchain environment. We explore the fundamentals, characteristics, and security challenges of NFT images. We examine the significance of quantum computing for digital security, highlighting vulnerabilities in

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Systematic Evaluation of Robustness Against Model Inversion Attacks on Split Learning However, it does not directly transmit the client’s original data to the server, and the intermediate features transmitted by the client allow an attacker to guess the original data via model inversion attacks. In this study, we conducted a quantitative evaluation to compare the performances of thr

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多山

Defending AirType Against Inference Attacks Using 3D In-Air Keyboard Layouts: Design and Evaluationinput. From a security perspective, however, recent works have demonstrated that these methods, such as in-air tapping, are vulnerable to inference attacks where an adversary is capable of reconstructing input in the virtual environment using low-level hand-tracking data with high accuracy. This pap

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Robust Training for Deepfake Detection Models Against Disruption-Induced Data Poisoningl threats. Consequently, deepfake detection has emerged as a crucial research area to deal with these rising threats. Additionally, deepfake disruption, a method that introduces proactive perturbations to genuine images to thwart deepfake generation, has arisen as a prospective defense mechanism. Wh

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Multi-class Malware Detection via Deep Graph Convolutional Networks Using TF-IDF-Based Attributed Canstantly evolves, exhibiting dynamic behavior and complexity, thus making it challenging to develop robust defense mechanisms. Traditional methods, such as signature-based and battery-monitoring approaches, struggle to detect emerging malware variants effectively. Recent advancements in deep learnin