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Strengthening deep neural networks: making ai less susceptible to adversarial...

989 грн

Описание

As deep neural networks (DNNs) become increasingly common in real-world
applications, the potential to deliberately "fool" them with data that
wouldnÃ¢??t trick a human presents a new attack vector. This practical book
examines real-world scenarios where DNNsÃ¢??the algorithms intrinsic to much
of AIÃ¢??are used daily to process image, audio, and video data.

Author Katy Warr considers attack motivations, the risks posed by this
adversarial input, and methods for increasing AI robustness to these attacks.
If youÃ¢??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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Susceptible to Adversarial Trickery, Katy Warr Ви можете по посиланню

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