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adversarial machine learning in real world computer vision systems adversarial machine learning in real world computer vision systems date june 16 2019 location long beach ca usa co located with cvpr 2019 abstract as computer vision models are being increasingly deployed in the real world including applications that require safety considerations such as self driving cars it is imperative that these models are robust and secure even when subject to adversarial inputs this workshop will focus on recent research and future directions for security problems in real world machine learning and computer vision systems we aim to bring together experts from the computer vision security and robust learning communities in an attempt to highlight recent work in this area as well as to clarify the foundations of secure machine learning we seek to come to a consensus on a rigorously framework to formulate adversarial machine learning problems in computer vision characterize the properties that ensure the security of perceptual models and evaluate the consequences under various adversarial models finally we hope to chart out important directions for future work and cross community collaborations including computer vision machine learning security and multimedia communities schedule the following is a tentative schedule and is subject to change prior to the workshop 8 40am opening remarks session 1 robust perception imitation and control 9 00am invited talk 1 yisong yue two vignettes in robust detection and adversarial analysis for control 9 30am invited talk 2 hao su towards attack agnostic defense for 2d and 3d recognition 10 00am contributed talk 1 nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks 10 15am coffee break session 2 improve model robustness against adversarial attacks 10 30am invited talk 3 alexander schwing robust gan training to capture priors when learning to anticipate 11 00am contributed talk 2 learning transferable adversarial examples via ghost networks 11 15am poster session 1 followed by break 12 00pm lunch session 3 vulnerabilities and robustness of machine learning models 1 15pm invited talk 4 song han defensive quantization when efficiency meets robustness 1 45pm invited talk 5 sergey levine robust perception imitation and reinforcement learning for embodied learning machines 2 15pm contributed talk 3 big but imperceptible adversarial perturbations via semantic manipulation 2 30pm coffee break session 4 adversarial machine learning in autonomous driving 2 45pm invited talk 6 trevor darrell explainable ai for vqa and driving 3 15pm invited talk 7 bolei zhou image manipulation from adversarial samples to gans 3 45pm contributed talk 4 attacking multiple object tracking using adversarial examples 4 00pm contributed talk 5 adversarial objects against lidar based autonomous driving systems 4 15pm poster session 2 schedule poster session 1 11 15am 12 00pm yunhan jia yantao lu junjie shen qi alfred chen zhenyu zhong and tao wei attacking multiple object tracking using adversarial examples 272 yingwei li song bai yuyin zhou cihang xie zhishuai zhang and alan yuille learning transferable adversarial examples via ghost networks 273 yulong cao chaowei xiao benjamin cyr yimeng zhou won park sara rampazzi qi alfred chen kevin fu and zhuoqing mao adversarial sensor attack on lidar based perception in autonomous driving 274 yunhan jia yantao lu senem velipasalar zhenyu zhong and tao wei enhancing cross task transferability of adversarial examples with dispersion reduction 274 kálmán szentannai jalal al afandi and andrás horváth mimosanet an unrobust neural network preventing model stealing 274 yang zhang hassan foroosh philip david and boqing gong camou learning a vehicle camouflage for physical adversarial attacks on object detectors in the wild 275 poster session 2 4 15pm 5 00pm yandong li lijun li liqiang wang tong zhang and boqing gong nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks 276 anand bhattad min jin chong kaizhao liang bo li and david forsyth big but imperceptible adversarial perturbations via semantic manipulation 277 thomas brunner frederik diehl and alois knoll copy and paste a simple but effective initialization method for black box adversarial attacks 278 abinaya kandasamy and venkatesh babu radhakrishnan adversarial frame 279 houpu yao zhe wang guangyu nie yassine mazboudi yezhou yang and yi ren augmenting model robustness with transformation invariant attacks 280 modar alfadly adel bibi and bernard ghanem analytical moment regularizer for gaussian robust networks 281 yulong cao chaowei xiao dawei yang jin fang ruigang yang mingyanliu bo li adversarial objects against lidar based autonomous driving systems 282 poster position pacific ball room poster size cvpr workshop the physical dimensions of the poster stands that will be available this year are 8 feet wide by 4 feet high please review the cvpr18 poster template for more details on how to prepare your poster organizing committee bo li li erran li david forsyth dawn song ramin zabih chaowei xiao program committee hadi abdullah uf fics yunhan jia baidu x lab yulong cao university of michigan octavian suciu university of maryland qi alfred chen university of california irvine cihang xie johns hopkins university yigitcan kaya university of maryland edward zhong baidu usa matthew wicker university of georgia linyi li university of illinois at urbana champaign yizheng chen columbia univeristy zhuolin yang shanghai jiao tong univerisity sixie yu washington university in st louis min jin chong university of illinois at urbana champaign eric wong carnegie mellon university yuxin wu fair warren he university of california berkeley xinchen yan university of michigan ann arbor mantas mazeika university of chicago kimin lee korea advanced institute of science and technology shreya shankar stanford university xinyun chen university of california berkeley kaizhao liang university of illinois urbana champaign fartash faghri university of toronto anand bhattad university of illinois at urbana champaign yunseok jang university of michigan xiaowei huang university of liverpool karl ni google llc kathrin grosse cispa saarland university chao yan vanderbilt university dawei yang university of michigan pin yu chen ibm important dates workshop paper submission deadline 5 20 2019 notification to authors 6 07 2019 camera ready deadline 6 12 2019 call for papers submission deadline may 20 2019 anywhere on earth aoe notification sent to authors june 7 2019 anywhere on earth aoe submission server https easychair org cfp advmlcv2019 the workshop will include contributed papers based on the pc s recommendation each paper accepted to the workshop will be allocated either a contributed talk or poster presentation submissions need to be anonymized the workshop allows submissions of papers that are under review or have been recently published in a conference or a journal the workshop will not have any official proceedings we invite submissions on any aspect of machine learning that relates to computer security and vice versa this includes but is not limited to test time exploratory attacks e g adversarial examples for neural nets training time causative attacks e g data poisoning physical attacks defenses differential privacy privacy preserving generative models game theoretic analysis on machine learning models manipulation of crowd sourcing systems sybil detection exploitable bugs in ml systems formal verification of ml systems model stealing misuse of ai and deep learning interpretable machine learning