DeepTest: Automated Testing of Deep-Neural-Network-Driven Autonomous Cars
暂无分享,去创建一个
Suman Jana | Baishakhi Ray | Kexin Pei | Yuchi Tian | S. Jana | Kexin Pei | Baishakhi Ray | Yuchi Tian
[1] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[2] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[3] B. Curless. Affine transformations , 1999 .
[4] Lakhmi C. Jain,et al. Recurrent Neural Networks: Design and Applications , 1999 .
[5] Yoshua Bengio,et al. Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .
[6] Phil McMinn,et al. Search‐based software test data generation: a survey , 2004, Softw. Test. Verification Reliab..
[7] Zongyuan Yang,et al. Metamorphic Testing and Its Applications , 2004 .
[8] Yoshua Bengio,et al. Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.
[9] Gail E. Kaiser,et al. Properties of Machine Learning Applications for Use in Metamorphic Testing , 2008, SEKE.
[10] Baowen Xu,et al. Application of Metamorphic Testing to Supervised Classifiers , 2009, 2009 Ninth International Conference on Quality Software.
[11] Corina S. Pasareanu,et al. A survey of new trends in symbolic execution for software testing and analysis , 2009, International Journal on Software Tools for Technology Transfer.
[12] Luca Pulina,et al. An Abstraction-Refinement Approach to Verification of Artificial Neural Networks , 2010, CAV.
[13] C. Spearman. The proof and measurement of association between two things. , 2015, International journal of epidemiology.
[14] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[15] Jan Hauke,et al. Comparison of Values of Pearson's and Spearman's Correlation Coefficients on the Same Sets of Data , 2011 .
[16] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[17] Myra B. Cohen,et al. An orchestrated survey of methodologies for automated software test case generation , 2013, J. Syst. Softw..
[18] W. B. Roberts,et al. Machine Learning: The High Interest Credit Card of Technical Debt , 2014 .
[19] Gang Wang,et al. Man vs. Machine: Practical Adversarial Detection of Malicious Crowdsourcing Workers , 2014, USENIX Security Symposium.
[20] อนิรุธ สืบสิงห์,et al. Data Mining Practical Machine Learning Tools and Techniques , 2014 .
[21] Pavel Laskov,et al. Practical Evasion of a Learning-Based Classifier: A Case Study , 2014, 2014 IEEE Symposium on Security and Privacy.
[22] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[23] Haider A. Alwzwazy,et al. Robust Convolutional Neural Networks for Image Recognition , 2015 .
[24] Jason Yosinski,et al. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Luca Rigazio,et al. Towards Deep Neural Network Architectures Robust to Adversarial Examples , 2014, ICLR.
[26] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[27] Chris Rowen,et al. Using Convolutional Neural Networks for Image Recognition By , 2015 .
[28] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[29] Uri Shaham,et al. Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization , 2015, ArXiv.
[30] Peter Kulchyski. and , 2015 .
[31] Lionel C. Briand,et al. Testing advanced driver assistance systems using multi-objective search and neural networks , 2016, 2016 31st IEEE/ACM International Conference on Automated Software Engineering (ASE).
[32] Xin Zhang,et al. End to End Learning for Self-Driving Cars , 2016, ArXiv.
[33] Ananthram Swami,et al. Crafting adversarial input sequences for recurrent neural networks , 2016, MILCOM 2016 - 2016 IEEE Military Communications Conference.
[34] Nina Narodytska,et al. Simple Black-Box Adversarial Perturbations for Deep Networks , 2016, ArXiv.
[35] Ananthram Swami,et al. Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks , 2015, 2016 IEEE Symposium on Security and Privacy (SP).
[36] John Salvatier,et al. Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.
[37] Antonio Criminisi,et al. Measuring Neural Net Robustness with Constraints , 2016, NIPS.
[38] Lujo Bauer,et al. Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition , 2016, CCS.
[39] Ananthram Swami,et al. The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).
[40] Patrick D. McDaniel,et al. Adversarial Perturbations Against Deep Neural Networks for Malware Classification , 2016, ArXiv.
[41] Martín Abadi,et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.
[42] Yanjun Qi,et al. Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers , 2016, NDSS.
[43] Yang Song,et al. Improving the Robustness of Deep Neural Networks via Stability Training , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[44] Vitaly Shmatikov,et al. Can we still avoid automatic face detection? , 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).
[45] Moustapha Cissé,et al. Parseval Networks: Improving Robustness to Adversarial Examples , 2017, ICML.
[46] Patrick D. McDaniel,et al. On the (Statistical) Detection of Adversarial Examples , 2017, ArXiv.
[47] Andrew M. Dai,et al. Adversarial Training Methods for Semi-Supervised Text Classification , 2016, ICLR.
[48] Atul Prakash,et al. Robust Physical-World Attacks on Machine Learning Models , 2017, ArXiv.
[49] Ryan R. Curtin,et al. Detecting Adversarial Samples from Artifacts , 2017, ArXiv.
[50] Patrick D. McDaniel,et al. Extending Defensive Distillation , 2017, ArXiv.
[51] Jan Hendrik Metzen,et al. On Detecting Adversarial Perturbations , 2017, ICLR.
[52] Ananthram Swami,et al. Practical Black-Box Attacks against Machine Learning , 2016, AsiaCCS.
[53] Junfeng Yang,et al. DeepXplore: Automated Whitebox Testing of Deep Learning Systems , 2017, SOSP.
[54] Dawn Xiaodong Song,et al. Delving into Transferable Adversarial Examples and Black-box Attacks , 2016, ICLR.
[55] Mykel J. Kochenderfer,et al. Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks , 2017, CAV.
[56] Dawn Song,et al. Robust Physical-World Attacks on Deep Learning Models , 2017, 1707.08945.
[57] Min Wu,et al. Safety Verification of Deep Neural Networks , 2016, CAV.
[58] Percy Liang,et al. Certified Defenses for Data Poisoning Attacks , 2017, NIPS.
[59] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[60] Dawn Xiaodong Song,et al. Adversarial Examples for Generative Models , 2017, 2018 IEEE Security and Privacy Workshops (SPW).
[61] Yanjun Qi,et al. Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks , 2017, NDSS.
[62] Tsong Yueh Chen,et al. Metamorphic Testing: A New Approach for Generating Next Test Cases , 2020, ArXiv.
[63] Ravishankar Chityala,et al. Affine Transformation , 2020, Image Processing and Acquisition using Python.