The Adversarial Robustness Toolbox (ART) = framework that uses generative adversarial neural networks (GANs) to protect deep learning models from security attacks
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GANs = the most popular form of generative models.
GAN-based attacks:
+White Box Attacks: The adversary has access to the training environment, knowledge of the training algorithm
+Black Box Attacks: The adversary has no additional knowledge
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GAN-based attacks:
+White Box Attacks: The adversary has access to the training environment, knowledge of the training algorithm
+Black Box Attacks: The adversary has no additional knowledge
2/⬇️
The goal of ART = to provide a framework to evaluate the robustness of a neural network.
The current version of ART focuses on four types of adversarial attacks:
+evasion
+inference
+extraction
+poisoning
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The current version of ART focuses on four types of adversarial attacks:
+evasion
+inference
+extraction
+poisoning
3/⬇️
ART is a generic Python library. It provides native integration with several deep learning frameworks such as @TensorFlow, @PyTorch, #Keras, @ApacheMXNet
@IBM open-sourced ART at github.com/IBM/adversaria….
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@IBM open-sourced ART at github.com/IBM/adversaria….
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If you'd like to find a concentrated coverage of ART, click the link below. You'll move to TheSequence Edge#7, our educational newsletter.
thesequence.substack.com/p/edge7
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thesequence.substack.com/p/edge7
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