Adversarial machine learning : (Record no. 91516)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02778 a2200217 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240207b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9783030997717 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.3 SRE-A |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Chivukula, Aneesh Sreevallabh |
| 245 ## - TITLE STATEMENT | |
| Title | Adversarial machine learning : |
| Remainder of title | attack surfaces, defence mechanisms, learning theories in artificial intelligence / |
| Statement of responsibility, etc. | Aneesh Sreevallabh Chivukula and others |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | Switzerland |
| Name of publisher, distributor, etc. | Springer |
| Date of publication, distribution, etc. | 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 302 p. |
| 500 ## - GENERAL NOTE | |
| General note | 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 about 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 characterise the security of learning systems using game theoretical adversarial deep learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms are 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, misclassification 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 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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Computer security |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Deep learning (Machine learning) |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Yang, Xinghao |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Liu, Bo |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Liu, Wei |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Zhou, Wanlei |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| Withdrawn status | |
| Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | 003-007 | BITS Pilani Hyderabad | BITS Pilani Hyderabad | Text & Reference Section (Student cannot borrow these books) | 17/01/2024 | 006.3 SRE-A | 48158 | 13/07/2024 | 17/01/2024 | Course Reference Books |