Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
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Jingyi Wang | Jun Sun | Peixin Zhang | Guoliang Dong | Xinyu Wang | Xinyu Wang | Jun Sun | Jingyi Wang | Peixin Zhang | Guoliang Dong
[1] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Geoffrey Zweig,et al. Achieving Human Parity in Conversational Speech Recognition , 2016, ArXiv.
[3] Ananthram Swami,et al. The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).
[4] David A. Wagner,et al. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples , 2018, ICML.
[5] Suman Jana,et al. DeepTest: Automated Testing of Deep-Neural-Network-Driven Autonomous Cars , 2017, 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE).
[6] Kevin Gimpel,et al. A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks , 2016, ICLR.
[7] Chenchen Liu,et al. Towards Robust Training of Neural Networks by Regularizing Adversarial Gradients , 2018, ArXiv.
[8] James Bailey,et al. Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality , 2018, ICLR.
[9] Kouichi Sakurai,et al. Attacking convolutional neural network using differential evolution , 2018, IPSJ Transactions on Computer Vision and Applications.
[10] Antonio Criminisi,et al. Measuring Neural Net Robustness with Constraints , 2016, NIPS.
[11] Samy Bengio,et al. Adversarial Machine Learning at Scale , 2016, ICLR.
[12] Aleksander Madry,et al. Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.
[13] Mykel J. Kochenderfer,et al. Towards Proving the Adversarial Robustness of Deep Neural Networks , 2017, FVAV@iFM.
[14] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[15] Matthias Bethge,et al. Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models , 2017, ICLR.
[16] R. Srikant,et al. Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks , 2017, ICLR.
[17] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[18] David A. Wagner,et al. Audio Adversarial Examples: Targeted Attacks on Speech-to-Text , 2018, 2018 IEEE Security and Privacy Workshops (SPW).
[19] Mark Harman,et al. An Analysis and Survey of the Development of Mutation Testing , 2011, IEEE Transactions on Software Engineering.
[20] Lujo Bauer,et al. Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition , 2016, CCS.
[21] Jinfeng Yi,et al. Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach , 2018, ICLR.
[22] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[23] Axel Legay,et al. Statistical Model Checking: An Overview , 2010, RV.
[24] Lei Ma,et al. DeepGauge: Comprehensive and Multi-Granularity Testing Criteria for Gauging the Robustness of Deep Learning Systems , 2018, ArXiv.
[25] Zhenlong Yuan,et al. Droid-Sec: deep learning in android malware detection , 2015, SIGCOMM 2015.
[26] Dan Boneh,et al. Ensemble Adversarial Training: Attacks and Defenses , 2017, ICLR.
[27] Jun Sun,et al. Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing , 2018, ArXiv.
[28] Daniel Kroening,et al. Concolic Testing for Deep Neural Networks , 2018, 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE).
[29] Junfeng Yang,et al. DeepXplore: Automated Whitebox Testing of Deep Learning Systems , 2017, SOSP.
[30] Mark Goadrich,et al. The relationship between Precision-Recall and ROC curves , 2006, ICML.
[31] Mykel J. Kochenderfer,et al. Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks , 2017, CAV.
[32] Gul A. Agha,et al. A Survey of Statistical Model Checking , 2018, ACM Trans. Model. Comput. Simul..
[33] Yang Song,et al. PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples , 2017, ICLR.
[34] Uri Shaham,et al. Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization , 2015, ArXiv.
[35] Min Wu,et al. Safety Verification of Deep Neural Networks , 2016, CAV.
[36] Patrick D. McDaniel,et al. Cleverhans V0.1: an Adversarial Machine Learning Library , 2016, ArXiv.
[37] Xin Zhang,et al. End to End Learning for Self-Driving Cars , 2016, ArXiv.
[38] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Shie Mannor,et al. Robustness and generalization , 2010, Machine Learning.
[40] Swarat Chaudhuri,et al. AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation , 2018, 2018 IEEE Symposium on Security and Privacy (SP).
[41] Inderjit S. Dhillon,et al. Towards Fast Computation of Certified Robustness for ReLU Networks , 2018, ICML.
[42] Lei Ma,et al. DeepMutation: Mutation Testing of Deep Learning Systems , 2018, 2018 IEEE 29th International Symposium on Software Reliability Engineering (ISSRE).
[43] Moustapha Cissé,et al. Countering Adversarial Images using Input Transformations , 2018, ICLR.
[44] Seyed-Mohsen Moosavi-Dezfooli,et al. DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Medhat A. Moussa,et al. Attacking Binarized Neural Networks , 2017, ICLR.
[46] Daniel Kroening,et al. Testing Deep Neural Networks , 2018, ArXiv.
[47] Huichen Lihuichen. DECISION-BASED ADVERSARIAL ATTACKS: RELIABLE ATTACKS AGAINST BLACK-BOX MACHINE LEARNING MODELS , 2017 .
[48] Jan Hendrik Metzen,et al. On Detecting Adversarial Perturbations , 2017, ICLR.
[49] Yanjun Qi,et al. Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks , 2017, NDSS.
[50] J. Andel. Sequential Analysis , 2022, The SAGE Encyclopedia of Research Design.
[51] Matthew Wicker,et al. Feature-Guided Black-Box Safety Testing of Deep Neural Networks , 2017, TACAS.
[52] Ryan R. Curtin,et al. Detecting Adversarial Samples from Artifacts , 2017, ArXiv.
[53] Mingyan Liu,et al. Spatially Transformed Adversarial Examples , 2018, ICLR.
[54] Ananthram Swami,et al. Practical Black-Box Attacks against Machine Learning , 2016, AsiaCCS.
[55] Alan L. Yuille,et al. Mitigating adversarial effects through randomization , 2017, ICLR.
[56] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[57] John C. Duchi,et al. Certifying Some Distributional Robustness with Principled Adversarial Training , 2017, ICLR.