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Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks.
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adversarial attacks model learning attack data machine models training image evasion example gradient malware proposed textstyle algorithms may security class
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Adversarial machine learning | is a | study of the attacks on machine learning algorithms | 0.90 | text |
| Google Brain's Nick Frosst point out that it is much easier to make self-driving cars miss stop signs by physically removing the sign itself | instance of | researchers | 0.80 | text |
| rather than creating adversarial examples | instance of | researchers | 0.80 | text |
| instance of | large tech companies | 0.80 | text | |
| Microsoft | instance of | large tech companies | 0.80 | text |
| and IBM have begun curating documentation | instance of | large tech companies | 0.80 | text |
| open source code bases to allow others to concretely assess the robustness of machine learning models | instance of | large tech companies | 0.80 | text |
| minimize the risk of adversarial attacks.ExamplesExamples include attacks in spam filtering | instance of | large tech companies | 0.80 | text |
| where spam messages are obfuscated through the misspelling of | instance of | large tech companies | 0.80 | text |
| GAMMA use genetic algorithms to inject benign content | instance of | Optimization-based attacks | 0.80 | text |
| surveillance | instance of | which are employed widely for real-world applications | 0.80 | text |
| autonomous vehicles | instance of | which are employed widely for real-world applications | 0.80 | text |
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