About the Book: 

Contents:

Part I - Overview of Adversarial Machine Learning

1. Introduction

2. Background and Notation

3. A Framework for Secure Learning

Part II - Causative Attacks on Machine Learning

4. Attacking a Hypersphere Learner

5. Availability Attack Case Study: SpamBayes

6. Integrity Attack Case Study: PCA Detector

Part III - Exploratory Attacks on Machine Learning

7. Privacy-Preserving Mechanisms for SVM Learning

8. Near-Optimal Evasion of Classifiers

Part IV - Future Directions in Adversarial Machine Learning

9. Adversarial Machine Learning Challenges

Part V - Appendixes

About the Authors:

Anthony D. Joseph is a Chancellor's Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He was formerly the Director of Intel Labs Berkeley.

Blaine Nelson is a Software Engineer in the Counter-Abuse Technology (CAT) team at Google. He previously worked at the University of Potsdam and the University of Tübingen.

Benjamin I. P. Rubinstein is an Associate Professor in Computing and Information Systems at the University of Melbourne. He has previously worked at Microsoft Research, Google Research, Yahoo! Research, Intel Labs Berkeley, and IBM Research.