NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles
作者： David A. Forsyth , Jiajun Lu , Hussein Sibai , Evan Fabry
DOI:
关键词:
摘要: … Recently, it was shown that physical adversarial examples exist: printing perturbed images then taking pictures of them would still result in misclassification. This raises security and …
harvard.edu
arxiv-vanity.com
arxiv.org参考文章(16)
Omar Fawzi, Pascal Frossard, Alhussein Fawzi, Analysis of classifiers' robustness to adversarial perturbations arXiv: Learning. ,(2015)
Luca Rigazio, Shixiang Gu, Towards Deep Neural Network Architectures Robust to Adversarial Examples arXiv: Learning. ,(2014)
Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, Brian Kingsbury, None, Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups IEEE Signal Processing Magazine. ,vol. 29, pp. 82- 97 ,(2012) , 10.1109/MSP.2012.2205597
Yanshuai Cao, David J. Fleet, Fartash Faghri, Sara Sabour, Adversarial Manipulation of Deep Representations arXiv: Computer Vision and Pattern Recognition. ,(2015)
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, None, Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples arXiv: Cryptography and Security. ,(2016)
Ian Goodfellow, Samy Bengio, Alexey Kurakin, Adversarial examples in the physical world arXiv: Computer Vision and Pattern Recognition. ,(2016)
Nicholas Carlini, David A. Wagner, Defensive Distillation is Not Robust to Adversarial Examples arXiv: Cryptography and Security. ,(2016)
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, Michael K. Reiter, Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition computer and communications security. pp. 1528- 1540 ,(2016) , 10.1145/2976749.2978392
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard, Universal Adversarial Perturbations computer vision and pattern recognition. pp. 86- 94 ,(2017) , 10.1109/CVPR.2017.17
Guy Katz, Clark Barrett, David L. Dill, Kyle Julian, Mykel J. Kochenderfer, Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks computer aided verification. pp. 97- 117 ,(2017) , 10.1007/978-3-319-63387-9_5