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ClearMark: Intuitive and Robust Model Watermarking via Transposed Model Training

, , и . ArXiv | arXiv:2310.16453v1, (октября 2023)

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ClearMark: Intuitive and Robust Model Watermarking via Transposed Model Training, , и . ArXiv | arXiv:2310.16453v1, (октября 2023)MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers, и . ACM Conference on Computer and Communications Security (CCS), (2023)MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers., и . CCS, стр. 1526-1540. ACM, (2023)ClearStamp: A Human-Visible and Robust Model-Ownership Proof based on Transposed Model Training, , и . To Appear in the 33rd USENIX Security Symposium (USENIX Security 2024), (2024)Ensuring Integrity of NVMe Offloaded Data in Large-Scale Machine Learning, , и . (2023)Security of NVMe Offloaded Data in Large-Scale Machine Learning., , и . ESORICS (4), том 14347 из Lecture Notes in Computer Science, стр. 143-163. Springer, (2023)DNNShield: Embedding Identifiers for Deep Neural Network Ownership Verification, , и . arXiv:2403.06581, (марта 2024)Evaluating Approaches for Online Resource Demand Estimation. University of Würzburg, Am Hubland, Informatikgebäude, 97074 Würzburg, Germany, Master Thesis, (сентября 2017)