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awesome-adversarial-machine-learning
A curated list of awesome adversarial machine learning resources
https://github.com/yenchenlin/awesome-adversarial-machine-learning
Last synced: 5 days ago
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Blogs
- Breaking Linear Classifiers on ImageNet
- Breaking things is easy
- Attacking Machine Learning with Adversarial Examples
- Robust Adversarial Examples
- A Brief Introduction to Adversarial Examples
- Training Robust Classifiers (Part 1)
- Adversarial Machine Learning Reading List
- Recommendations for Evaluating Adversarial Example Defenses
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Papers
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General
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Attack
- DeepFool: a simple and accurate method to fool deep neural networks - Dezfooli et al., CVPR 2016
- The Limitations of Deep Learning in Adversarial Settings
- Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
- Adversarial Examples In The Physical World
- Delving into Transferable Adversarial Examples and Black-box Attacks
- Towards Evaluating the Robustness of Neural Networks
- Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples
- Privacy and machine learning: two unexpected allies?
- Adversarial attacks on neural network policies
- Tactics of Adversarial Attacks on Deep Reinforcement Learning Agents
- Delving into adversarial attacks on deep policies
- Adversarial Examples for Semantic Segmentation and Object Detection
- Adversarial examples for generative models
- Audio Adversarial Examples: Targeted Attacks on Speech-to-Text
- Adversarial Examples for Evaluating Reading Comprehension Systems
- Adversarial Examples for Semantic Segmentation and Object Detection
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Defence
- Adversarial Machine Learning At Scale
- Ensemble Adversarial Training: Attacks and Defenses
- Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
- Extending Defensive Distillation
- PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples
- Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight
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Regularization
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Others
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Talks
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Others
- Do Statistical Models Understand the World?
- Classifiers under Attack
- Adversarial Examples in Machine Learning
- Poisoning Behavioral Malware Clustering
- Is Data Clustering in Adversarial Settings Secure?
- Poisoning complete-linkage hierarchical clustering
- Is Feature Selection Secure against Training Data Poisoning?
- Adversarial Feature Selection Against Evasion Attacks
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Licenses
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Others
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