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2020 – today
- 2022
[c42]Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, Nicolas Papernot:
Unrolling SGD: Understanding Factors Influencing Machine Unlearning. EuroS&P 2022: 303-319- [i69]Adam Dziedzic, Muhammad Ahmad Kaleem, Yu Shen Lu, Nicolas Papernot:
Increasing the Cost of Model Extraction with Calibrated Proof of Work. CoRR abs/2201.09243 (2022) - [i68]Shimaa Ahmed, Yash Wani, Ali Shahin Shamsabadi, Mohammad Yaghini, Ilia Shumailov, Nicolas Papernot, Kassem Fawaz:
Pipe Overflow: Smashing Voice Authentication for Fun and Profit. CoRR abs/2202.02751 (2022) - [i67]Ali Shahin Shamsabadi, Brij Mohan Lal Srivastava, Aurélien Bellet, Nathalie Vauquier, Emmanuel Vincent, Mohamed Maouche, Marc Tommasi, Nicolas Papernot:
Differentially Private Speaker Anonymization. CoRR abs/2202.11823 (2022) - [i66]Anvith Thudi, Ilia Shumailov, Franziska Boenisch, Nicolas Papernot:
Bounding Membership Inference. CoRR abs/2202.12232 (2022) - [i65]Natalie Dullerud, Karsten Roth, Kimia Hamidieh, Nicolas Papernot, Marzyeh Ghassemi:
Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning. CoRR abs/2203.12748 (2022) - [i64]Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot:
On the Difficulty of Defending Self-Supervised Learning against Model Extraction. CoRR abs/2205.07890 (2022) - [i63]Stephan Rabanser, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic, Nicolas Papernot:
Selective Classification Via Neural Network Training Dynamics. CoRR abs/2205.13532 (2022) - [i62]Mikel Bober-Irizar, Ilia Shumailov, Yiren Zhao, Robert Mullins, Nicolas Papernot:
Architectural Backdoors in Neural Networks. CoRR abs/2206.07840 (2022) - [i61]Yue Gao, Ilia Shumailov, Kassem Fawaz, Nicolas Papernot:
On the Limitations of Stochastic Pre-processing Defenses. CoRR abs/2206.09491 (2022) - [i60]Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, Florian Tramèr:
The Privacy Onion Effect: Memorization is Relative. CoRR abs/2206.10469 (2022) - 2021
- [c41]Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, Úlfar Erlingsson:
Tempered Sigmoid Activations for Deep Learning with Differential Privacy. AAAI 2021: 9312-9321 - [c40]Jean-Baptiste Truong, Pratyush Maini, Robert J. Walls, Nicolas Papernot:
Data-Free Model Extraction. CVPR 2021: 4771-4780 - [c39]Hui Xu, Guanpeng Li, Homa Alemzadeh, Rakesh Bobba
, Varun Chandrasekaran, David E. Evans, Nicolas Papernot, Karthik Pattabiraman, Florian Tramèr:
Fourth International Workshop on Dependable and Secure Machine Learning - DSML 2021. DSN Workshops 2021: xvi - [c38]Ilia Shumailov, Yiren Zhao, Daniel Bates, Nicolas Papernot, Robert D. Mullins, Ross Anderson:
Sponge Examples: Energy-Latency Attacks on Neural Networks. EuroS&P 2021: 212-231 - [c37]Vinith M. Suriyakumar, Nicolas Papernot, Anna Goldenberg, Marzyeh Ghassemi:
Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings. FAccT 2021: 723-734 - [c36]Christopher A. Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, Xiao Wang:
CaPC Learning: Confidential and Private Collaborative Learning. ICLR 2021 - [c35]Pratyush Maini, Mohammad Yaghini, Nicolas Papernot:
Dataset Inference: Ownership Resolution in Machine Learning. ICLR 2021 - [c34]Christopher A. Choquette-Choo, Florian Tramèr, Nicholas Carlini, Nicolas Papernot:
Label-Only Membership Inference Attacks. ICML 2021: 1964-1974 - [c33]David Khachaturov, Ilia Shumailov, Yiren Zhao, Nicolas Papernot, Ross J. Anderson:
Markpainting: Adversarial Machine Learning meets Inpainting. ICML 2021: 5409-5419 - [c32]Mingyue Yang, David Lie, Nicolas Papernot:
Accelerating Symbolic Analysis for Android Apps. ASE Workshops 2021: 47-52 - [c31]Ilia Shumailov, Zakhar Shumaylov, Dmitry Kazhdan, Yiren Zhao, Nicolas Papernot, Murat A. Erdogdu, Ross J. Anderson:
Manipulating SGD with Data Ordering Attacks. NeurIPS 2021: 18021-18032 - [c30]Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, Nicolas Papernot:
Machine Unlearning. IEEE Symposium on Security and Privacy 2021: 141-159 - [c29]Hadi Abdullah, Kevin Warren, Vincent Bindschaedler, Nicolas Papernot, Patrick Traynor:
SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems. IEEE Symposium on Security and Privacy 2021: 730-747 - [c28]Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini:
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. IEEE Symposium on Security and Privacy 2021: 866-882 - [c27]Hengrui Jia, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Anvith Thudi, Varun Chandrasekaran, Nicolas Papernot:
Proof-of-Learning: Definitions and Practice. IEEE Symposium on Security and Privacy 2021: 1039-1056 - [c26]Hengrui Jia, Christopher A. Choquette-Choo, Varun Chandrasekaran, Nicolas Papernot:
Entangled Watermarks as a Defense against Model Extraction. USENIX Security Symposium 2021: 1937-1954 - [i59]Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini:
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. CoRR abs/2101.04535 (2021) - [i58]Christopher A. Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, Xiao Wang:
CaPC Learning: Confidential and Private Collaborative Learning. CoRR abs/2102.05188 (2021) - [i57]Hengrui Jia, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Anvith Thudi, Varun Chandrasekaran, Nicolas Papernot:
Proof-of-Learning: Definitions and Practice. CoRR abs/2103.05633 (2021) - [i56]Ilia Shumailov, Zakhar Shumaylov, Dmitry Kazhdan, Yiren Zhao, Nicolas Papernot, Murat A. Erdogdu, Ross J. Anderson:
Manipulating SGD with Data Ordering Attacks. CoRR abs/2104.09667 (2021) - [i55]Pratyush Maini, Mohammad Yaghini, Nicolas Papernot:
Dataset Inference: Ownership Resolution in Machine Learning. CoRR abs/2104.10706 (2021) - [i54]David Khachaturov, Ilia Shumailov, Yiren Zhao, Nicolas Papernot, Ross J. Anderson:
Markpainting: Adversarial Machine Learning meets Inpainting. CoRR abs/2106.00660 (2021) - [i53]Nicholas Boucher, Ilia Shumailov, Ross J. Anderson, Nicolas Papernot:
Bad Characters: Imperceptible NLP Attacks. CoRR abs/2106.09898 (2021) - [i52]Adelin Travers, Lorna Licollari, Guanghan Wang, Varun Chandrasekaran, Adam Dziedzic, David Lie, Nicolas Papernot:
On the Exploitability of Audio Machine Learning Pipelines to Surreptitious Adversarial Examples. CoRR abs/2108.02010 (2021) - [i51]Varun Chandrasekaran, Hengrui Jia, Anvith Thudi, Adelin Travers, Mohammad Yaghini, Nicolas Papernot:
SoK: Machine Learning Governance. CoRR abs/2109.10870 (2021) - [i50]Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, Nicolas Papernot:
Unrolling SGD: Understanding Factors Influencing Machine Unlearning. CoRR abs/2109.13398 (2021) - [i49]Gabriel Deza, Adelin Travers, Colin Rowat, Nicolas Papernot:
Interpretability in Safety-Critical FinancialTrading Systems. CoRR abs/2109.15112 (2021) - [i48]Nicolas Papernot, Thomas Steinke:
Hyperparameter Tuning with Renyi Differential Privacy. CoRR abs/2110.03620 (2021) - [i47]Anvith Thudi, Hengrui Jia, Ilia Shumailov, Nicolas Papernot:
On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning. CoRR abs/2110.11891 (2021) - [i46]Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot:
When the Curious Abandon Honesty: Federated Learning Is Not Private. CoRR abs/2112.02918 (2021) - 2020
- [c25]Homa Alemzadeh, Rakesh Bobba, Varun Chandrasekaran, David E. Evans, Nicolas Papernot, Karthik Pattabiraman, Florian Tramèr:
Third International Workshop on Dependable and Secure Machine Learning - DSML 2020. DSN Workshops 2020: x - [c24]Andrew Boutros, Mathew Hall, Nicolas Papernot, Vaughn Betz:
Neighbors From Hell: Voltage Attacks Against Deep Learning Accelerators on Multi-Tenant FPGAs. FPT 2020: 103-111 - [c23]Kalpesh Krishna, Gaurav Singh Tomar, Ankur P. Parikh, Nicolas Papernot, Mohit Iyyer:
Thieves on Sesame Street! Model Extraction of BERT-based APIs. ICLR 2020 - [c22]Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen:
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. ICML 2020: 9561-9571 - [c21]Jieyu Lin, Kristina Dzeparoska, Sai Qian Zhang, Alberto Leon-Garcia, Nicolas Papernot:
On the Robustness of Cooperative Multi-Agent Reinforcement Learning. SP Workshops 2020: 62-68 - [c20]Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, Nicolas Papernot:
High Accuracy and High Fidelity Extraction of Neural Networks. USENIX Security Symposium 2020: 1345-1362 - [i45]Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen:
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. CoRR abs/2002.04599 (2020) - [i44]Sanghyun Hong, Varun Chandrasekaran, Yigitcan Kaya, Tudor Dumitras, Nicolas Papernot:
On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping. CoRR abs/2002.11497 (2020) - [i43]Hengrui Jia, Christopher A. Choquette-Choo, Nicolas Papernot:
Entangled Watermarks as a Defense against Model Extraction. CoRR abs/2002.12200 (2020) - [i42]Jieyu Lin, Kristina Dzeparoska, Sai Qian Zhang, Alberto Leon-Garcia, Nicolas Papernot:
On the Robustness of Cooperative Multi-Agent Reinforcement Learning. CoRR abs/2003.03722 (2020) - [i41]Ilia Shumailov, Yiren Zhao, Daniel Bates, Nicolas Papernot, Robert D. Mullins, Ross J. Anderson:
Sponge Examples: Energy-Latency Attacks on Neural Networks. CoRR abs/2006.03463 (2020) - [i40]Hadi Abdullah, Kevin Warren, Vincent Bindschaedler, Nicolas Papernot, Patrick Traynor:
SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems. CoRR abs/2007.06622 (2020) - [i39]Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, Úlfar Erlingsson:
Tempered Sigmoid Activations for Deep Learning with Differential Privacy. CoRR abs/2007.14191 (2020) - [i38]Christopher A. Choquette-Choo, Florian Tramèr, Nicholas Carlini, Nicolas Papernot:
Label-Only Membership Inference Attacks. CoRR abs/2007.14321 (2020) - [i37]Baiwu Zhang, Jin Peng Zhou, Ilia Shumailov, Nicolas Papernot:
Not My Deepfake: Towards Plausible Deniability for Machine-Generated Media. CoRR abs/2008.09194 (2020) - [i36]Vinith M. Suriyakumar, Nicolas Papernot, Anna Goldenberg, Marzyeh Ghassemi:
Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings. CoRR abs/2010.06667 (2020) - [i35]Ryan Sheatsley, Nicolas Papernot, Michael J. Weisman, Gunjan Verma, Patrick D. McDaniel:
Adversarial Examples in Constrained Domains. CoRR abs/2011.01183 (2020) - [i34]Jean-Baptiste Truong, Pratyush Maini, Robert J. Walls, Nicolas Papernot:
Data-Free Model Extraction. CoRR abs/2011.14779 (2020) - [i33]Andrew Boutros, Mathew Hall, Nicolas Papernot, Vaughn Betz:
Neighbors From Hell: Voltage Attacks Against Deep Learning Accelerators on Multi-Tenant FPGAs. CoRR abs/2012.07242 (2020)
2010 – 2019
- 2019
- [j3]Dan Boneh, Andrew J. Grotto, Patrick D. McDaniel, Nicolas Papernot:
How Relevant Is the Turing Test in the Age of Sophisbots? IEEE Secur. Priv. 17(6): 64-71 (2019) - [c19]Nicholas Frosst, Nicolas Papernot, Geoffrey E. Hinton:
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss. ICML 2019: 2012-2020 - [c18]David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel:
MixMatch: A Holistic Approach to Semi-Supervised Learning. NeurIPS 2019: 5050-5060 - [i32]Nicholas Frosst, Nicolas Papernot, Geoffrey E. Hinton:
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss. CoRR abs/1902.01889 (2019) - [i31]Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian J. Goodfellow, Aleksander Madry, Alexey Kurakin:
On Evaluating Adversarial Robustness. CoRR abs/1902.06705 (2019) - [i30]Jörn-Henrik Jacobsen, Jens Behrmann, Nicholas Carlini, Florian Tramèr, Nicolas Papernot:
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness. CoRR abs/1903.10484 (2019) - [i29]David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel:
MixMatch: A Holistic Approach to Semi-Supervised Learning. CoRR abs/1905.02249 (2019) - [i28]Dan Boneh, Andrew J. Grotto, Patrick D. McDaniel, Nicolas Papernot:
How Relevant is the Turing Test in the Age of Sophisbots? CoRR abs/1909.00056 (2019) - [i27]Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, Nicolas Papernot:
High-Fidelity Extraction of Neural Network Models. CoRR abs/1909.01838 (2019) - [i26]Zhengli Zhao, Nicolas Papernot, Sameer Singh, Neoklis Polyzotis, Augustus Odena:
Improving Differentially Private Models with Active Learning. CoRR abs/1910.01177 (2019) - [i25]Kalpesh Krishna, Gaurav Singh Tomar, Ankur P. Parikh, Nicolas Papernot, Mohit Iyyer:
Thieves on Sesame Street! Model Extraction of BERT-based APIs. CoRR abs/1910.12366 (2019) - [i24]Nicholas Carlini, Úlfar Erlingsson, Nicolas Papernot:
Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications. CoRR abs/1910.13427 (2019) - [i23]Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, Nicolas Papernot:
Machine Unlearning. CoRR abs/1912.03817 (2019) - 2018
- [j2]Ian J. Goodfellow, Patrick D. McDaniel, Nicolas Papernot:
Making machine learning robust against adversarial inputs. Commun. ACM 61(7): 56-66 (2018) - [c17]Nicolas Papernot:
A Marauder's Map of Security and Privacy in Machine Learning: An overview of current and future research directions for making machine learning secure and private. AISec@CCS 2018: 1 - [c16]Z. Berkay Celik, Patrick D. McDaniel, Rauf Izmailov, Nicolas Papernot, Ryan Sheatsley, Raquel Alvarez, Ananthram Swami:
Detection under Privileged Information. AsiaCCS 2018: 199-206 - [c15]Nicolas Papernot, Patrick D. McDaniel, Arunesh Sinha, Michael P. Wellman:
SoK: Security and Privacy in Machine Learning. EuroS&P 2018: 399-414 - [c14]Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Úlfar Erlingsson:
Scalable Private Learning with PATE. ICLR 2018 - [c13]Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, Patrick D. McDaniel:
Ensemble Adversarial Training: Attacks and Defenses. ICLR (Poster) 2018 - [c12]Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alexey Kurakin, Ian J. Goodfellow, Jascha Sohl-Dickstein:
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans. NeurIPS 2018: 3914-3924 - [i22]Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian J. Goodfellow, Jascha Sohl-Dickstein:
Adversarial Examples that Fool both Human and Computer Vision. CoRR abs/1802.08195 (2018) - [i21]Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Úlfar Erlingsson:
Scalable Private Learning with PATE. CoRR abs/1802.08908 (2018) - [i20]Nicolas Papernot, Patrick D. McDaniel:
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning. CoRR abs/1803.04765 (2018) - [i19]Wieland Brendel, Jonas Rauber, Alexey Kurakin, Nicolas Papernot, Behar Veliqi, Marcel Salathé, Sharada P. Mohanty, Matthias Bethge:
Adversarial Vision Challenge. CoRR abs/1808.01976 (2018) - [i18]Nicolas Papernot:
A Marauder's Map of Security and Privacy in Machine Learning. CoRR abs/1811.01134 (2018) - 2017
- [c11]Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami:
Practical Black-Box Attacks against Machine Learning. AsiaCCS 2017: 506-519 - [c10]Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang:
On the Protection of Private Information in Machine Learning Systems: Two Recent Approches. CSF 2017: 1-6 - [c9]Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, Patrick D. McDaniel:
Adversarial Examples for Malware Detection. ESORICS (2) 2017: 62-79 - [c8]Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, Pieter Abbeel:
Adversarial Attacks on Neural Network Policies. ICLR (Workshop) 2017 - [c7]Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, Kunal Talwar:
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. ICLR 2017 - [i17]Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, Pieter Abbeel:
Adversarial Attacks on Neural Network Policies. CoRR abs/1702.02284 (2017) - [i16]Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, Patrick D. McDaniel:
On the (Statistical) Detection of Adversarial Examples. CoRR abs/1702.06280 (2017) - [i15]Florian Tramèr, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, Patrick D. McDaniel:
The Space of Transferable Adversarial Examples. CoRR abs/1704.03453 (2017) - [i14]Nicolas Papernot, Patrick D. McDaniel:
Extending Defensive Distillation. CoRR abs/1705.05264 (2017) - [i13]Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Dan Boneh, Patrick D. McDaniel:
Ensemble Adversarial Training: Attacks and Defenses. CoRR abs/1705.07204 (2017) - [i12]Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang:
On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches. CoRR abs/1708.08022 (2017) - 2016
- [j1]Patrick D. McDaniel, Nicolas Papernot, Z. Berkay Celik:
Machine Learning in Adversarial Settings. IEEE Secur. Priv. 14(3): 68-72 (2016) - [c6]Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, Ananthram Swami:
The Limitations of Deep Learning in Adversarial Settings. EuroS&P 2016: 372-387 - [c5]Z. Berkay Celik, Nan Hu, Yun Li, Nicolas Papernot, Patrick D. McDaniel, Robert J. Walls, Jeff Rowe, Karl N. Levitt, Novella Bartolini, Thomas F. La Porta, Ritu Chadha:
Mapping sample scenarios to operational models. MILCOM 2016: 7-12 - [c4]Nicolas Papernot, Patrick D. McDaniel, Ananthram Swami, Richard E. Harang:
Crafting adversarial input sequences for recurrent neural networks. MILCOM 2016: 49-54 - [c3]Nicolas Papernot, Patrick D. McDaniel, Xi Wu, Somesh Jha, Ananthram Swami:
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks. IEEE Symposium on Security and Privacy 2016: 582-597 - [i11]Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami:
Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples. CoRR abs/1602.02697 (2016) - [i10]Z. Berkay Celik, Patrick D. McDaniel, Rauf Izmailov, Nicolas Papernot, Ananthram Swami:
Building Better Detection with Privileged Information. CoRR abs/1603.09638 (2016) - [i9]Nicolas Papernot, Patrick D. McDaniel, Ananthram Swami, Richard E. Harang:
Crafting Adversarial Input Sequences for Recurrent Neural Networks. CoRR abs/1604.08275 (2016) - [i8]Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow:
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples. CoRR abs/1605.07277 (2016) - [i7]Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, Patrick D. McDaniel:
Adversarial Perturbations Against Deep Neural Networks for Malware Classification. CoRR abs/1606.04435 (2016) - [i6]Nicolas Papernot, Patrick D. McDaniel:
On the Effectiveness of Defensive Distillation. CoRR abs/1607.05113 (2016) - [i5]Ian J. Goodfellow, Nicolas Papernot, Patrick D. McDaniel:
cleverhans v0.1: an adversarial machine learning library. CoRR abs/1610.00768 (2016) - [i4]Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, Kunal Talwar:
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. CoRR abs/1610.05755 (2016) - [i3]Nicolas Papernot, Patrick D. McDaniel, Arunesh Sinha, Michael P. Wellman:
Towards the Science of Security and Privacy in Machine Learning. CoRR abs/1611.03814 (2016) - 2015
- [c2]Nicolas Papernot, Patrick D. McDaniel, Robert J. Walls:
Enforcing agile access control policies in relational databases using views. MILCOM 2015: 7-12 - [i2]Nicolas Papernot, Patrick D. McDaniel, Xi Wu, Somesh Jha, Ananthram Swami:
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks. CoRR abs/1511.04508 (2015) - [i1]Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, Ananthram Swami:
The Limitations of Deep Learning in Adversarial Settings. CoRR abs/1511.07528 (2015) - 2014
- [c1]Patrick D. McDaniel, Trent Jaeger, Thomas F. La Porta, Nicolas Papernot, Robert J. Walls, Alexander Kott, Lisa M. Marvel, Ananthram Swami, Prasant Mohapatra, Srikanth V. Krishnamurthy, Iulian Neamtiu:
Security and Science of Agility. MTD@CCS 2014: 13-19
Coauthor Index