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This paper asks whether free-text keystroke biometrics can be studied without collecting real user data by simulating typing behavior with an agent-based model (ABM)
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This paper conducts a comprehensive ethical audit comparing synthetic personas generated by three large language models (GPT4o, Gemini 1.5 Pro, Deepseek 2.5) to human-authored self-descriptions, wi…
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This paper addresses key challenges in mouse dynamics-based user authentication, namely determining the sufficient data volume, balancing accuracy and practicality with segment length, and enhancin…
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This paper tackles a practical security gap in Li-Fi / visible light communication: how to authenticate transmitters without adding heavyweight cryptography or breaking IEEE 802.15.7 compatibility
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This paper addresses the urgent need to verify human authorship of digital content in an internet increasingly dominated by AI-generated synthetic media
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This paper argues that conventional logging and network-centric security situational awareness miss an important gap: they can tell you that an identity logged in or a token was used, but not wheth…
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This paper asks a very specific question that many continuous-authentication systems sidestep: if gaze is a strong biometric today, does it still work months later in VR? The authors evaluate eye-t…
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This paper addresses the challenge of generating synthetic face datasets with high intra-class diversity while preserving identity, to train face recognition (FR) models without relying on privacy-…
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This paper addresses the critical challenge of 3D human pose estimation under real-world deficiency-aware conditions such as occlusions, noise interference, and missing viewpoints
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This paper conducts a comparative empirical study of three statistical keystroke dynamics authentication methods—Gaussian Mixture Models (GMM), Mahalanobis Distance, and Gunetti Picardi’s Distance …
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This paper addresses the significant privacy threat posed by browser fingerprinting, which enables cross-site user tracking and identification beyond traditional cookie-based methods
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This paper addresses the problem of online learning to rank under the cascade model in the presence of adversarial corruptions manipulating user feedback
