Download Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence PDF Free - Full Version
Download Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence by Aneesh Sreevallabh Chivukula, Xinghao Yang, Bo Liu, Wei Liu, Wanlei Zhou in PDF format completely FREE. No registration required, no payment needed. Get instant access to this valuable resource on PDFdrive.to!
About Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence
A critical challenge in deep learning is the vulnerability of deep learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in computer vision; natural language processing; and cybersecurity with regard to multidimensional, textual and image data, sequence data, and temporal data. In turn, we assess the robustness properties of deep learning networks to produce a taxonomy of adversarial examples that characterises the security of learning systems using game theoretical adversarial deep learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms is also reviewed. We propose new adversary types for game theoretical objectives in non-stationary computational learning environments. Proper quantification of the hypothesis set in the decision problems of our research leads to various functional problems, oracular problems, sampling tasks, and optimization problems. We also address the defence mechanisms currently available for deep learning models deployed in real-world environments. The learning theories used in these defence mechanisms concern data representations, feature manipulations, misclassifications costs, sensitivity landscapes, distributional robustness, and complexity classes of the adversarial deep learning algorithms and their applications. In closing, we propose future research directions in adversarial deep learning applications for resilient learning system design and review formalized learning assumptions concerning the attack surfaces and robustness characteristics of artificial intelligence applications so as to deconstruct the contemporary adversarial deep learning designs. Given its scope, the book will be of interest to Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers whose work involves the design and application of Adversarial Deep Learning.
Detailed Information
| Author: | Aneesh Sreevallabh Chivukula, Xinghao Yang, Bo Liu, Wei Liu, Wanlei Zhou |
|---|---|
| Publication Year: | 2023 |
| ISBN: | 9783030997717 |
| Pages: | 314 |
| Language: | English |
| File Size: | 3.301 |
| Format: | |
| Price: | FREE |
Safe & Secure Download - No registration required
Why Choose PDFdrive for Your Free Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence Download?
- 100% Free: No hidden fees or subscriptions required for one book every day.
- No Registration: Immediate access is available without creating accounts for one book every day.
- Safe and Secure: Clean downloads without malware or viruses
- Multiple Formats: PDF, MOBI, Mpub,... optimized for all devices
- Educational Resource: Supporting knowledge sharing and learning
Frequently Asked Questions
Is it really free to download Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence PDF?
Yes, on https://PDFdrive.to you can download Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence by Aneesh Sreevallabh Chivukula, Xinghao Yang, Bo Liu, Wei Liu, Wanlei Zhou completely free. We don't require any payment, subscription, or registration to access this PDF file. For 3 books every day.
How can I read Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence on my mobile device?
After downloading Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence PDF, you can open it with any PDF reader app on your phone or tablet. We recommend using Adobe Acrobat Reader, Apple Books, or Google Play Books for the best reading experience.
Is this the full version of Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence?
Yes, this is the complete PDF version of Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence by Aneesh Sreevallabh Chivukula, Xinghao Yang, Bo Liu, Wei Liu, Wanlei Zhou. You will be able to read the entire content as in the printed version without missing any pages.
Is it legal to download Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence PDF for free?
https://PDFdrive.to provides links to free educational resources available online. We do not store any files on our servers. Please be aware of copyright laws in your country before downloading.
The materials shared are intended for research, educational, and personal use in accordance with fair use principles.
