Strengthening Deep Neural Networks
Strengthening Deep Neural Networks
Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you're a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you.
Delve into DNNs and discover how they could be tricked by adversarial input
Investigate methods used to generate adversarial input capable of fooling DNNs
Explore real-world scenarios and model the adversarial threat
Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data
Examine some ways in which AI might become better at mimicking human perception in years to come
PRP: 380.73 Lei
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Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you're a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you.
Delve into DNNs and discover how they could be tricked by adversarial input
Investigate methods used to generate adversarial input capable of fooling DNNs
Explore real-world scenarios and model the adversarial threat
Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data
Examine some ways in which AI might become better at mimicking human perception in years to come
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