Inference Attack
88 papers with code • 0 benchmarks • 2 datasets
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MinneApple
Synthetic Keystroke
Most implemented papers
Membership Inference Attacks From First Principles
privacytrustlab/ml_privacy_meter
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A membership inference attack allows an adversary to query a trained machine learning model to predict whether or not a particular example was contained in the model's training dataset.
Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment
By simulating the attack mechanism as the safety test, SafeCompress can automatically compress a big model to a small one following the dynamic sparse training paradigm.
Dissecting Distribution Inference
iamgroot42/dissecting_distribution_inference
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A distribution inference attack aims to infer statistical properties of data used to train machine learning models.
Understanding Membership Inferences on Well-Generalized Learning Models
Membership Inference Attack (MIA) determines the presence of a record in a machine learning model's training data by querying the model.
Machine Learning with Membership Privacy using Adversarial Regularization
hyhmia/BlindMI
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In this paper, we focus on such attacks against black-box models, where the adversary can only observe the output of the model, but not its parameters.
Privacy Risks of Securing Machine Learning Models against Adversarial Examples
inspire-group/privacy-vs-robustness
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To perform the membership inference attacks, we leverage the existing inference methods that exploit model predictions.
Reconstruction and Membership Inference Attacks against Generative Models
SAP-samples/security-research-membership-inference-against-generative-networks
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We present two information leakage attacks that outperform previous work on membership inference against generative models.
GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models
In addition, we propose the first generic attack model that can be instantiated in a large range of settings and is applicable to various kinds of deep generative models.
RIGA: Covert and Robust White-Box Watermarking of Deep Neural Networks
White-box watermarking algorithms have the advantage that they do not impact the accuracy of the watermarked model.
An Empirical Study on the Intrinsic Privacy of SGD
microsoft/intrinsic-private-sgd
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Introducing noise in the training of machine learning systems is a powerful way to protect individual privacy via differential privacy guarantees, but comes at a cost to utility.
