Offense - Adversarial Threats/Attacks

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• Boolean Satisfiability (SAT) Problem/Satisfiability Modulo Theories (SMT) Solvers
• Defenses Against Adversarial Examples for Deep Neural Networks

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• 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.