PPT-CS 404/504 Special Topics: Adversarial Machine Learning

Dr Alex Vakanski Lecture 6 GANs for Adversarial Machine Learning Lecture Outline Mohamed Hassan presentation Introduction to Generative Adversarial Networks GANs Jeffrey Wyrick presentation. huangintelcom Anthony D Joseph UC Berkeley adjcsberkeleyedu Blaine Nelson University of T57596bingen blainenelsonwsiiuni tuebingende Benjamin I P Rubinstein Microsoft Research benrubinsteinmicrosoftcom J D Tygar UC Berkeley tygarcsberkeleyedu ABSTRAC Machine: Adversarial Detection . of Malicious . Crowdsourcing Workers . Gang . Wang. , Tianyi Wang, Haitao . Zheng, Ben . Y. Zhao . UC Santa Barbara. gangw@cs.ucsb.edu. Machine Learning for Security. Whedon. Dr. David Lavery. Fall 2014. PH 308, M 600-900. . . . Click on the screen capture above to go the blog entry.. Joss Does the Comics. Special Topics in Film Studies: Joss . Whedon. . . . Statistical Relational AI. Daniel Lowd. University of Oregon. Outline. Why do we need adversarial modeling?. Because of the dream of AI. Because of current reality. Because of possible dangers. Our initial approach and results. Dr. David Lavery, Fall 2016. Entities and Groups. Special Topics in Popular Culture, Fall 2016. Brotherhood without Banners. Special Topics in Popular Culture, Fall 2016. Children of the Forest. Special Topics in Popular Culture, Fall 2016. Dr. David Lavery, Fall 2016. Directors. Special Topics in Popular Culture, Fall 2016. Game of Thrones . Directors. Special Topics in Popular Culture, Fall 2016. Game of Thrones . Directors. Special Topics in Popular Culture, Fall 2016. —An Introduction. Binghui. Wang, Computer Engineering. Supervisor: Neil . Zhenqiang. Gong. 01/13/2017. Outline. Machine Learning (ML) . Adversarial . ML. Attack . Taxonomy. Capability. Adversarial Training . Adversarial examples. Ostrich!. Adversarial examples. Ostrich!. Intriguing properties of neural networks. . Christian . Szegedy. , . Wojciech. . Zaremba. , Ilya . Sutskever. , Joan Bruna, . Dumitru. Boosting. Nhan Nguyen. Computer Science and Engineering Dept.. Boosting. Method for converting rules of thumb into a prediction rule.. Rule . of thumb. ?. Method?. Binary Classification. X: set of all possible instances or examples. . Florian Tramèr. Intel, Santa Clara, CA. August 30. th. 2018. First they came for images…. The Deep Learning Revolution. The Deep Learning Revolution. And then everything else…. The ML Revolution. Deep Learning and Security Workshop 2017. Chang Liu. UC Berkeley. Deep Learning and Security is a trending topic in academia in 2017. Best Papers in Security Conferences. Towards Evaluating the Robustness of Neural Networks (Oakland 2017 Best Student Paper). Generative Adversarial Networks (GANs). Generative Adversarial Networks (GANs). Goodfellow. et al (2014) . https://arxiv.org/abs/1406.2661. Minimize distance between the distributions of real data and generated samples. Dr. Alex Vakanski. Lecture 1. Introduction to Adversarial Machine Learning. . Lecture Outline. Machine Learning (ML). Adversarial ML (AML). Adversarial examples. Attack taxonomy. Common adversarial attacks. Dr. Alex Vakanski. Lecture . 10. AML in . Cybersecurity – Part I:. Malware Detection and Classification. . Lecture Outline. Machine Learning in cybersecurity. Adversarial Machine Learning in cybersecurity.