PPT-Adversaries Adversarial examples

Adversarial examples Ostrich Adversarial examples Ostrich Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna  Dumitru. Cormac. Flanagan & Stephen Freund. UC Santa Cruz Williams . College. PLDI 2010. Slides by Michelle Goodstein. LBA Reading Group, June 2 2010. Motivation. Multi-threaded programs often contain data races. 1. IN. THE PRESENCE OF. LIMITED. ADVERSARIES. Background – Communication Scenario. 2. Alice. Bob. 011100110110111101110011. Calvin. Bad guy. Encoder. Decoder. (Adversarial). Noisy Channel .  .  . 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. 2012 . VP Debate. - SNL. Tina Fey/Sarah Palin. - SNL. Sarah and Hillary . - SNL. Clinton . pioneered the use of town meetings and television entertainment programs as a means of communicating directly with voters in the 1992 election. 1. IN. THE PRESENCE OF. LIMITED. ADVERSARIES. Sidharth. . Jaggi. , The Chinese University of Hong Kong. Between Shannon and Hamming: . Codes against limited adversaries. 3. Qiaosheng Zhang. Mayank. 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. (. 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). 2WE CAN ADVERSARY BEHAVIOR THROUGH PERSISTENT INTEGRATED OPERATIONSSUPERIORITY IN THE PHYSICAL IN NO SMALL PART DEPENDS ON SUPERIORITY IN CYBERSPACEMilitary superiority in the air land sea and space EXPERIMENTS”. Paper # 27. Vagan Terziyan,. Mariia Golovianko, Svitlana Gryshko & Tuure Tuunanen. ISM 2020. International Conference on Industry 4.0. and Smart Manufacturing. 25 November, 2020, . Dr. Alex Vakanski. Lecture 6. GANs for Adversarial Machine Learning. Lecture Outline. Mohamed Hassan presentation. Introduction to Generative Adversarial Networks (GANs). Jeffrey Wyrick presentation. S. . Adu. , . P.K. . Gyekye. , Nuclear Regulatory Authority, Ghana. A. . Sfetsos, . I. . Tsourounakis. , M. Varvayianni, N. Catsaros, NCSR Demokritos, Greece . M.K. Snell, . Sandia National Laboratories, USA. 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.