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Found 9 papers, 3 papers with code
Deletion Inference, Reconstruction, and Compliance in Machine (Un)Learning
Privacy attacks on machine learning models aim to identify the data that is used to train such models.
Adversarial Sample Detection for Speaker Verification by Neural Vocoders
We also show that the neural vocoder adopted in the detection framework is dataset-independent.
Learning and Certification under Instance-targeted Poisoning
In this paper, we study PAC learnability and certification of predictions under instance-targeted poisoning attacks, where the adversary who knows the test instance may change a fraction of the training set with the goal of fooling the learner at the test instance.
STLnet: Signal Temporal Logic Enforced Multivariate Recurrent Neural Networks
In this paper, we develop a new temporal logic-based learning framework, STLnet, which guides the RNN learning process with auxiliary knowledge of model properties, and produces a more robust model for improved future predictions.
Exploring the Naturalness of Buggy Code with Recurrent Neural Networks
Statistical language models are powerful tools which have been used for many tasks within natural language processing.
Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention has been paid to black-box attacks, which are more realistic scenarios.
DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples
By identifying and removing unnecessary features in a DNN model, DeepCloak limits the capacity an attacker can use generating adversarial samples and therefore increase the robustness against such inputs.
A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models
Estimating multiple sparse Gaussian Graphical Models (sGGMs) jointly for many related tasks (large $K$) under a high-dimensional (large $p$) situation is an important task.
A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Examples
Most machine learning classifiers, including deep neural networks, are vulnerable to adversarial examples.
