Difference between revisions of "Offense - Adversarial Threats/Attacks"
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| + | * [http://arxiv.org/abs/1412.6572 Explaining and Harnessing Adversarial Examples | Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy] | ||
| + | * [http://blog.openai.com/adversarial-example-research/ Attacking Machine Learning with Adversarial Examples | OpenAI - By Ian Goodfellow, Nicolas Papernot, Sandy Huang, Yan Duan, Pieter Abbeel & Jack Clark] | ||
| + | * [http://www.cleverhans.io/ cleverhans - library for benchmarking the vulnerability of machine learning models to adversarial examples blog] | ||
* [http://github.com/nababora/advML Adversarial Machine Learning for Anti-Malware Software | nababora @ GitHub] | * [http://github.com/nababora/advML Adversarial Machine Learning for Anti-Malware Software | nababora @ GitHub] | ||
* [http://evademl.org/ EvadeML.org | University of Virginia] | * [http://evademl.org/ EvadeML.org | University of Virginia] | ||
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** [http://pralab.diee.unica.it/en/ALFASVMLib adversarial label flip attacks against Support Vector Machines (ALFASVMLib) | PRA Lab] | ** [http://pralab.diee.unica.it/en/ALFASVMLib 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. | ||
<youtube>4rFOkpI0Lcg</youtube> | <youtube>4rFOkpI0Lcg</youtube> | ||
Revision as of 19:46, 11 June 2018
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- Explaining and Harnessing Adversarial Examples | Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy
- 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
- EvadeML.org | University of Virginia
- AdversariaLib: An Open-source Library for the Security Evaluation of Machine Learning Algorithms Under Attack .pdf
- Pattern Recognition and Applications Lab (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.
