PPT-CS 502 Directed Studies: Adversarial Machine Learning

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. 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. 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. Presenters: Pooja Harekoppa, Daniel Friedman. Explaining and Harnessing Adversarial Examples. Ian J. . Goodfellow. , Jonathon . Shlens. and Christian . Szegedy. Google Inc., Mountain View, CA. Highlights . ML Reading . Group. Xiao Lin. Jul. 22 2015. I. . Goodfellow. , J. . Pouget-Abadie. , M. Mirza, B. Xu, D. . Warde. -Farley, S. . Ozair. , A. . Courville. and Y. . Bengio. . . "Generative adversarial nets." . for . edge detection. Z. Zeng Y.K. Yu, K.H. Wong. In . IEEE iciev2018, International Conference on Informatics, Electronics & Vision '. June,kitakyushu. exhibition center, japan, 25~29, 2018. (. Presented by Tammy Repine. Single Family Housing Direct Loan Division. Revision Date: June 19, 2017 . Section 502 Direct Loan Program Overview.   . . Authorized . by the Housing Act of . 1949. Provides affordable housing loans to eligible . Akrit Mohapatra. ECE Department, Virginia Tech. What are GANs?. System of . two neural networks competing against each other in a zero-sum game framework. . They were first introduced by . Ian Goodfellow. Use . adversarial learning . to suppress the effects of . domain variability. (e.g., environment, speaker, language, dialect variability) in acoustic modeling (AM).. Deficiency: domain classifier treats deep features uniformly without discrimination.. 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). Rev May 29 2020on reverseIndependentStudyn991Directed Study UndergraduateIndependent Research n995 IDNameName Instructor Subject/CourseTerm YearCredit Hours Title of Project STUDENT SIGNATUREI have Using Adult Education Strategies to Actively Cope with Chronic Illness. By Dr. Kristin . Brittain. & Dr. Valerie Bryan. Introduction. Due to the complexity of the health care system, patients are increasingly being asked to take more responsibility for their own self-care. . DATAWorks. 2021 - . Test & Evaluation Methods for Emerging Technology and Domains. 04/16/21. Galen Mullins. Gautam . Vallabha. Aurora Schmidt. Sam Barham. Sean McDaniel. Eric . Naber. Tyler Young. Dr. Alex Vakanski. Lecture 6. GANs for Adversarial Machine Learning. Lecture Outline. Mohamed Hassan presentation. Introduction to Generative Adversarial Networks (GANs). Jeffrey Wyrick presentation. Attacks. Haotian Wang. Ph.D. . . Student. University of Idaho. Computer Science. Outline. Introduction. Defense . a. gainst . Adversarial Attack Methods. Gradient Masking/Obfuscation. Robust Optimization.