Offense - Adversarial Threats/Attacks

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• Cybersecurity
• Defense - Anomaly Detection
• Capabilities

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• Attacking Machine Learning with Adversarial Examples | OpenAI - By Ian Goodfellow, Nicolas Papernot, Sandy Huang, Yan Duan, Pieter Abbeel & Jack Clark
• Cleverhans - library for benchmarking the vulnerability of machine learning models to adversarial examples blog
• Adversarial Machine Learning for Anti-Malware Software | nababora @ GitHub
• Deep-pwning/Metasploit | Clarence Chio
• EvadeML.org | University of Virginia
• AdversariaLib: An Open-source Library for the Security Evaluation of Machine Learning Algorithms Under Attack
• Pattern Recognition and Applications Lab (PRA Lab)
  • AdversariaLib | PRA Lab
  • adversarial label flip attacks against Support Vector Machines (ALFASVMLib) | PRA Lab

Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they’re like optical illusions for machines. Myth: An attacker must have access to the model to generate adversarial examples. Fact: Adversarial examples generalize across models trained to perform the same task, even if those models have different architectures and were trained on a different training set. This means an attacker can train their own model, generate adversarial examples against it, and then deploy those adversarial examples against a model they do not have access to. -Deep Learning Adversarial Examples – Clarifying Misconceptions | Goodfellow et al.

Papers

Papernot et al. Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

Papernot et al. The Limitations of Deep Learning in Adversarial Settings

Papernot et al. Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples

Papernot et al. Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks

Papernot et al. Adversarial Examples in Machine Learning

Goodfellow et al. Explaining and Harnessing Adversarial Examples

Biggio et al. Support Vector Machines Under Adversarial Label Noise

Biggio et al. Poisoning Attacks against Support Vector Machines

Eykholt et al. Robust Physical-World Attacks on Deep Learning Visual Classification

Szegedy et al. Intriguing properties of neural networks

Grosse et al. Adversarial Perturbations Against Deep Neural Networks for Malware Classification

Nguyen et al. Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images

Xu et al. Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers

Kantchelian et al. Evasion and Hardening of Tree Ensemble Classifiers

Ororbia II et al. Unifying Adversarial Training Algorithms with Flexible Deep Data Gradient Regularization

Jin et al. Robust Convolutional Neural Networks under Adversarial Noise

Marco Barreno et al. Can Machine Learning Be Secure?

J.D. Tygar, Ling Huang et al. Adversarial Machine Learning

Huang Xiao et al. Adversarial and Secure Machine Learning

William Uther et al.Adversarial Reinforcement Learning

Alexey Kurakin et al. Adversarial examples in the physical world

Pavel Laskov et al. Machine Learning in Adversarial Environments

Weaponizing Machine Learning

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