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Libraries designed to construct white-box and black-box attacks using model gradients to find vulnerabilities.
Distinct from White Box Attacks: Existing candidates are either for cryptographic lattices, DoS attacks, or general learning, not ML gradient attacks.
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Cleverhans is an adversarial machine learning library and toolkit designed to generate adversarial examples, incorporate them into training loops, and benchmark the resilience of machine learning models. It provides a gradient-based attack framework for constructing both white-box and black-box attacks to identify model misclassifications. The project includes capabilities for model robustness benchmarking, allowing users to evaluate and verify how models resist evasion attacks and malicious input perturbations. It also facilitates adversarial training to increase a model's resistance to pert
Implements a framework for constructing white-box and black-box attacks using gradients to identify misclassifications.