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Energy-Efficient and Adversarially Resilient Underwater Object Detection via Adaptive Vision Transformers. [PDF]
Sensors (Basel)Li L, Zhang G, Zhou Y.
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Secure and fault tolerant cloud based framework for medical image storage and retrieval in a distributed environment. [PDF]
Sci RepAmaithi Rajan A, V V, M A, R PK.
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ASQ-PHI: An adversarial synthetic data benchmark for clinical de-identification and search utility. [PDF]
Data BriefWeatherhead J, Golovko G, McCaffrey P.
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OntoSecAI: Ontology-driven security automation for AI-enabled systems. [PDF]
PLoS OneUllah U, Haleem M, Ullah A.
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Objectivity in an adversarial system
Medico-Legal Journal, 2020The Rt Hon Lady Justice Heather Hallet
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Jailbreaking GPT-4V via Self-Adversarial Attacks with System Prompts
arXiv.org, 2023Existing work on jailbreak Multimodal Large Language Models (MLLMs) has focused primarily on adversarial examples in model inputs, with less attention to vulnerabilities, especially in model API. To fill the research gap, we carry out the following work:
Yuanwei Wu +4 more
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IEEE Transactions on Smart Grid, 2023
The network attack detection model based on machine learning (ML) has received extensive attention and research in PMU measurement data protection of power systems. However, well-trained ML-based detection models are vulnerable to adversarial attacks. By
Rong Huang, Yuancheng Li
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The network attack detection model based on machine learning (ML) has received extensive attention and research in PMU measurement data protection of power systems. However, well-trained ML-based detection models are vulnerable to adversarial attacks. By
Rong Huang, Yuancheng Li
semanticscholar +1 more source