spring-epfl / trickster
Library and experiments for attacking machine learning in discrete domains
☆45Updated 2 years ago
Alternatives and similar repositories for trickster:
Users that are interested in trickster are comparing it to the libraries listed below
- Plausible looking adversarial examples for text classification☆92Updated 6 years ago
- Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks [NeurIPS 2019]☆50Updated 4 years ago
- A community-run reference for state-of-the-art adversarial example defenses.☆49Updated 5 months ago
- Code for the paper "Weight Poisoning Attacks on Pre-trained Models" (ACL 2020)☆140Updated 3 years ago
- Interfaces for defining Robust ML models and precisely specifying the threat models under which they claim to be secure.☆62Updated 5 years ago
- ☆37Updated 5 years ago
- ☆32Updated 7 years ago
- Concealed Data Poisoning Attacks on NLP Models☆21Updated last year
- CodeBase for Paper: "Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers", / Interactive Demo @☆76Updated last year
- Interval attacks (adversarial ML)☆21Updated 5 years ago
- EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples☆40Updated 6 years ago
- Code for "Testing Robustness Against Unforeseen Adversaries"☆81Updated 7 months ago
- Circumventing the defense in "Ensemble Adversarial Training: Attacks and Defenses"☆39Updated 7 years ago
- Task-agnostic universal black-box attacks on computer vision neural network via procedural noise (CCS'19)☆55Updated 4 years ago
- Code corresponding to the paper "Adversarial Examples are not Easily Detected..."☆85Updated 7 years ago
- Implementation code for the paper "Generating Natural Language Adversarial Examples"☆169Updated 5 years ago
- Game-Theoretic Adversarial Machine Learning Library☆58Updated 6 years ago
- Code for "Detecting Adversarial Samples from Artifacts" (Feinman et al., 2017)☆108Updated 7 years ago
- Code release for the ICML 2019 paper "Are generative classifiers more robust to adversarial attacks?"☆23Updated 5 years ago
- Code for "Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?"☆46Updated last year
- Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]☆32Updated 4 years ago
- Supervised Local Modeling for Interpretability☆28Updated 6 years ago
- ☆121Updated 3 years ago
- A concise primer on Differential Privacy☆28Updated 4 years ago
- ☆51Updated 6 years ago
- [ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples☆67Updated 2 years ago
- Detecting Adversarial Examples in Deep Neural Networks☆66Updated 6 years ago
- ☆27Updated 4 years ago
- to add☆20Updated 5 years ago
- Adversarial Examples: Attacks and Defenses for Deep Learning☆32Updated 6 years ago