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Extracting Training Data from Diffusion Models


Jan 30, 2023
Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace

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Unlocking High-Accuracy Differentially Private Image Classification through Scale


Apr 28, 2022
Soham De, Leonard Berrada, Jamie Hayes, Samuel L. Smith, Borja Balle

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Reconstructing Training Data with Informed Adversaries


Jan 13, 2022
Borja Balle, Giovanni Cherubin, Jamie Hayes

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Learning to be adversarially robust and differentially private


Jan 06, 2022
Jamie Hayes, Borja Balle, M. Pawan Kumar

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* Preliminary work appeared at PPML 2021 

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Towards transformation-resilient provenance detection of digital media


Nov 14, 2020
Jamie Hayes, Krishnamurthy, Dvijotham, Yutian Chen, Sander Dieleman, Pushmeet Kohli, Norman Casagrande

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Adaptive Traffic Fingerprinting: Large-scale Inference under Realistic Assumptions


Oct 19, 2020
Vasilios Mavroudis, Jamie Hayes

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Toward Robustness and Privacy in Federated Learning: Experimenting with Local and Central Differential Privacy


Sep 08, 2020
Mohammad Naseri, Jamie Hayes, Emiliano De Cristofaro

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Provable trade-offs between private & robust machine learning


Jun 08, 2020
Jamie Hayes

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Extensions and limitations of randomized smoothing for robustness guarantees


Jun 07, 2020
Jamie Hayes

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* CVPR 2020 Workshop on Adversarial Machine Learning in Computer Vision 

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Unique properties of adversarially trained linear classifiers on Gaussian data


Jun 06, 2020
Jamie Hayes

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