As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—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...
As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—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 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
Welcome to Tanto Cyber - Cybersecurity & IT Solutions! 🚀
To access all the features and facilities we provide, please log in first using your account.
By using TantoCyber.com, you agree to comply with the applicable rules for the safety and convenience of everyone.
Thank you for being part of our community! 🔒✨
Tanto Cyber: A platform sharing free eBooks & tech insights, focusing on cybersecurity and knowledge.
