PPT-Adversarial

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. WILLIAMSON IBM T J Watson Research Center Yorktown Heights New York Abstract We consider packet routing when packets are injected continuously into a network We develop an adversarial theory of queuing aimed at addressing some of the restrictions in 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. 16, 123-149 (1992) Adversarial Problem Solving: Modeling an Opponent Using Explanatory Coherence PAUL TI-IAGARD Princeton University In adversarial problem solving (APS), one He who can modvy hi INQUISITORIAL. -Judge can ask the accused questions. -Accused must answer questions from lawyers as well as the judge. -Accused may not be presumed innocent and the burden of proof may be on them to prove their innocence. etc. Convnets. (optimize weights to predict bus). bus. Convnets. (optimize input to predict ostrich). ostrich. Work on Adversarial examples by . Goodfellow. et al. , . Szegedy. et. al., etc.. Generative Adversarial Networks (GAN) [. Nets. İlke Çuğu 1881739. NIPS 2014 . Ian. . Goodfellow. et al.. At a . glance. (. http://www.kdnuggets.com/2017/01/generative-adversarial-networks-hot-topic-machine-learning.html. ). Idea. . Behind. Andrea W. Richa. Arizona State University. SIROCCO'13, Andrea Richa. 1. Motivation. Channel availability hard to model:. Mobility. Packet injection. Temporary Obstacles. Background noise. Physical Interference. —An Introduction. Binghui. Wang, Computer Engineering. Supervisor: Neil . Zhenqiang. Gong. 01/13/2017. Outline. Machine Learning (ML) . Adversarial . ML. Attack . Taxonomy. Capability. Adversarial Training . Presenters: Pooja Harekoppa, Daniel Friedman. Explaining and Harnessing Adversarial Examples. Ian J. . Goodfellow. , Jonathon . Shlens. and Christian . Szegedy. Google Inc., Mountain View, CA. Highlights . 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, . Florian Tramèr. Stanford University, Google, ETHZ. ML suffers from . adversarial. . examples.. 2. 90% Tabby Cat. 100% Guacamole. Adversarial noise. Robust classification is . hard! . 3. Clean. Adversarial (. Attacks. Haotian Wang. Ph.D. . . Student. University of Idaho. Computer Science. Outline. Introduction. Defense . a. gainst . Adversarial Attack Methods. Gradient Masking/Obfuscation. Robust Optimization. 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.