PPT-Defenses Against Adversarial

Attacks Haotian Wang PhD Student University of Idaho Computer Science Outline Introduction Defense a gainst Adversarial Attack Methods Gradient MaskingObfuscation Robust Optimization. Aram Harrow (UW -> MIT). Matt Hastings (Duke/MSR). Anup Rao (UW). The origins of determinism. Theorem [von Neumann]:. There exists a constant . p>0. such that for any circuit C there exists a circuit C’ such that. 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. Page 16-2A(7) DEFENSES TO DISCIPLINE and removals-against thousands of letter carriers. In some cases, the disciplined letter carrier accepts the punishment as having been warranted and does not Eugene Calaoagan. UERM Medicine 2011. M. ature and immature defenses. Immature Defenses. Acting Out . Expressing an unconscious wish or impulse through action to avoid being conscious of the accompanying effect. 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) [. 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. , . endoparasites. Social parasites: Brood Parasitism. Parasite–Predator spectrum. Microparasites. : Viruses, Bacteria. Macroparasites. : “Worms” . Cestodes. Parasitoids: . Ichneumonid. wasps . Adversarial examples. Ostrich!. Adversarial examples. Ostrich!. Intriguing properties of neural networks. . Christian . Szegedy. , . Wojciech. . Zaremba. , Ilya . Sutskever. , Joan Bruna, . Dumitru. 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." . 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). 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, .