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Awesome GitHub RepositoriesModel Functionality Extraction
Replicates a target model's behavior by querying it and training a substitute model on the responses.
Distinct from Extraction Model Evaluation: Distinct from Extraction Model Evaluation: focuses on the attack technique of replicating model behavior, not evaluating extraction quality.
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Awesome Model Functionality Extraction GitHub Repositories
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- trusted-ai/adversarial-robustness-toolbox
Trusted-AI/adversarial-robustness-toolbox
6,056Ver en GitHubThe Adversarial Robustness Toolbox (ART) is an open-source library that provides a unified framework for evaluating, defending, and certifying machine learning models against adversarial threats. It wraps models from any framework behind a common estimator interface, enabling composable pipelines for attack generation, defense application, robustness certification, and privacy auditing across evasion, poisoning, and extraction threats. The library distinguishes itself by covering the full adversarial ML security lifecycle within a single toolkit. It supports gradient-based adversarial example
Replicates target model behavior by querying it and training a substitute on the responses.
Pythonadversarial-attacksadversarial-examplesadversarial-machine-learning
