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- An adversarial example library for constructing attacks, building defenses, and benchmarking both
PrivatePrompts
Publicmachine-unlearning
Publicimpartiality_viz
Publicsc-nntd
PublicFRAUD-Detect
Publicdataset-inference
Publicmonte-carlo-adv
PublicForging
Publiccapc-demo
Publicmodel-extraction-iclr
Publicssl-attacks-defenses
Publichuggingface-transformers
Publiccapc-iclr
PublicCaPC is a method that enables collaborating parties to improve their own local heterogeneous machine learning models in a setting where both confidentiality and privacy need to be preserved to prevent explicit and implicit sharing of private data.Zest-Model-Distance
Publicunrolling-sgd
PublicSAT-selective-cls
Publiccapc-privacy
PublicProof-of-Learning
Publicentangled-watermark
Publicdeepfake_attribution
Public