Florian Tramèr

ORCID

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 117

Top Venues

CoRR

ICLR

NeurIPS

USENIX Security Symposium

Publications

Shanglun Feng, Florian Tramèr
Privacy Backdoors: Stealing Data with Corrupted Pretrained Models. CoRR (2024)
Francesco Pinto, Nathalie Rauschmayr, Florian Tramèr, Philip Torr, Federico Tombari
Extracting Training Data from Document-Based VQA Models. CoRR (2024)
Jonathan Hayase, Ema Borevkovic, Nicholas Carlini, Florian Tramèr, Milad Nasr
Query-Based Adversarial Prompt Generation. CoRR (2024)
Debeshee Das, Jie Zhang, Florian Tramèr
Blind Baselines Beat Membership Inference Attacks for Foundation Models. CoRR (2024)
Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J. Pappas, Florian Tramèr, Hamed Hassani, Eric Wong
JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models. CoRR (2024)
Robert Hönig, Javier Rando, Nicholas Carlini, Florian Tramèr
Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI. CoRR (2024)
Edoardo Debenedetti, Javier Rando, Daniel Paleka, Silaghi Fineas Florin, Dragos Albastroiu, Niv Cohen, Yuval Lemberg, Reshmi Ghosh, Rui Wen, Ahmed Salem, Giovanni Cherubin, Santiago Zanella Béguelin, Robin Schmid, Victor Klemm, Takahiro Miki, Chenhao Li, Stefan Kraft, Mario Fritz, Florian Tramèr, Sahar Abdelnabi, Lea Schönherr
Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition. CoRR (2024)
Nicholas Carlini, Daniel Paleka, Krishnamurthy (Dj) Dvijotham, Thomas Steinke, Jonathan Hayase, A. Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, Florian Tramèr
Stealing Part of a Production Language Model. CoRR (2024)
Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, Benjamin L. Edelman, Zhaowei Zhang, Mario Günther, Anton Korinek, José Hernández-Orallo, Lewis Hammond, Eric J. Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Yoshua Bengio, Danqi Chen, Samuel Albanie, Tegan Maharaj, Jakob N. Foerster, Florian Tramèr, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger
Foundational Challenges in Assuring Alignment and Safety of Large Language Models. CoRR (2024)
Michael Aerni, Jie Zhang, Florian Tramèr
Evaluations of Machine Learning Privacy Defenses are Misleading. CoRR (2024)
Edoardo Debenedetti, Giorgio Severi, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Eric Wallace, Nicholas Carlini, Florian Tramèr
Privacy Side Channels in Machine Learning Systems. USENIX Security Symposium (2024)
Lukas Fluri, Daniel Paleka, Florian Tramèr
Evaluating Superhuman Models with Consistency Checks. SaTML (2024)
Javier Rando, Francesco Croce, Krystof Mitka, Stepan Shabalin, Maksym Andriushchenko, Nicolas Flammarion, Florian Tramèr
Competition Report: Finding Universal Jailbreak Backdoors in Aligned LLMs. CoRR (2024)
Javier Rando, Florian Tramèr
Universal Jailbreak Backdoors from Poisoned Human Feedback. ICLR (2024)
Edoardo Debenedetti, Nicholas Carlini, Florian Tramèr
Evading Black-box Classifiers Without Breaking Eggs. SaTML (2024)
Fredrik Nestaas, Edoardo Debenedetti, Florian Tramèr
Adversarial Search Engine Optimization for Large Language Models. CoRR (2024)
Edoardo Debenedetti, Jie Zhang, Mislav Balunovic, Luca Beurer-Kellner, Marc Fischer, Florian Tramèr
AgentDojo: A Dynamic Environment to Evaluate Attacks and Defenses for LLM Agents. CoRR (2024)
Nikhil Kandpal, Matthew Jagielski, Florian Tramèr, Nicholas Carlini
Backdoor Attacks for In-Context Learning with Language Models. CoRR (2023)
Lukas Fluri, Daniel Paleka, Florian Tramèr
Evaluating Superhuman Models with Consistency Checks. CoRR (2023)
Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo, Daniel Paleka, Will Pearce, Hyrum Anderson, Andreas Terzis, Kurt Thomas, Florian Tramèr
Poisoning Web-Scale Training Datasets is Practical. CoRR (2023)
Matthew Jagielski, Milad Nasr, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini, Florian Tramèr
Students Parrot Their Teachers: Membership Inference on Model Distillation. NeurIPS (2023)
Nicholas Carlini, Florian Tramèr, Krishnamurthy (Dj) Dvijotham, Leslie Rice, Mingjie Sun, J. Zico Kolter
(Certified!!) Adversarial Robustness for Free! ICLR (2023)
Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace
Extracting Training Data from Diffusion Models. USENIX Security Symposium (2023)
Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr, Nicholas Carlini
Counterfactual Memorization in Neural Language Models. NeurIPS (2023)
Javier Rando, Florian Tramèr
Universal Jailbreak Backdoors from Poisoned Human Feedback. CoRR (2023)
Edoardo Debenedetti, Nicholas Carlini, Florian Tramèr
Evading Black-box Classifiers Without Breaking Eggs. CoRR (2023)
Milad Nasr, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Eric Wallace, Florian Tramèr, Katherine Lee
Scalable Extraction of Training Data from (Production) Language Models. CoRR (2023)
Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, Andreas Terzis
Tight Auditing of Differentially Private Machine Learning. CoRR (2023)
Keane Lucas, Matthew Jagielski, Florian Tramèr, Lujo Bauer, Nicholas Carlini
Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators. CoRR (2023)
Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini
Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of Privacy. INLG (2023)
Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan R. Ullman
SNAP: Efficient Extraction of Private Properties with Poisoning. SP (2023)
Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace
Extracting Training Data from Diffusion Models. CoRR (2023)
Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Irena Gao, Anas Awadalla, Pang Wei Koh, Daphne Ippolito, Katherine Lee, Florian Tramèr, Ludwig Schmidt
Are aligned neural networks adversarially aligned? CoRR (2023)
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, Chiyuan Zhang
Quantifying Memorization Across Neural Language Models. ICLR (2023)
Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, Andreas Terzis
Tight Auditing of Differentially Private Machine Learning. USENIX Security Symposium (2023)
Chawin Sitawarin, Florian Tramèr, Nicholas Carlini
Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems. ICML (2023)
Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Irena Gao, Pang Wei Koh, Daphne Ippolito, Florian Tramèr, Ludwig Schmidt
Are aligned neural networks adversarially aligned? NeurIPS (2023)
Edoardo Debenedetti, Giorgio Severi, Nicholas Carlini, Christopher A. Choquette-Choo, Matthew Jagielski, Milad Nasr, Eric Wallace, Florian Tramèr
Privacy Side Channels in Machine Learning Systems. CoRR (2023)
Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Guha Thakurta, Nicolas Papernot, Chiyuan Zhang
Measuring Forgetting of Memorized Training Examples. ICLR (2023)
Evani Radiya-Dixit, Sanghyun Hong, Nicholas Carlini, Florian Tramèr
Data Poisoning Won't Save You From Facial Recognition. ICLR (2022)
Roland S. Zimmermann, Wieland Brendel, Florian Tramèr, Nicholas Carlini
Increasing Confidence in Adversarial Robustness Evaluations. CoRR (2022)
Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, Florian Tramèr
The Privacy Onion Effect: Memorization is Relative. NeurIPS (2022)
Xuechen Li, Florian Tramèr, Percy Liang, Tatsunori Hashimoto
Large Language Models Can Be Strong Differentially Private Learners. ICLR (2022)
Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Chiyuan Zhang
Measuring Forgetting of Memorized Training Examples. CoRR (2022)
Hannah Brown, Katherine Lee, Fatemehsadat Mireshghallah, Reza Shokri, Florian Tramèr
What Does it Mean for a Language Model to Preserve Privacy? FAccT (2022)
Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan R. Ullman
SNAP: Efficient Extraction of Private Properties with Poisoning. CoRR (2022)
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, Florian Tramèr
Membership Inference Attacks From First Principles. IEEE Symposium on Security and Privacy (2022)
Florian Tramèr, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong, Nicholas Carlini
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets. CCS (2022)
Javier Rando, Daniel Paleka, David Lindner, Lennart Heim, Florian Tramèr
Red-Teaming the Stable Diffusion Safety Filter. CoRR (2022)
Florian Tramèr
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them. ICML (2022)
Hannah Brown, Katherine Lee, Fatemehsadat Mireshghallah, Reza Shokri, Florian Tramèr
What Does it Mean for a Language Model to Preserve Privacy? CoRR (2022)
Nicholas Carlini, Florian Tramèr, Krishnamurthy Dvijotham, J. Zico Kolter
(Certified!!) Adversarial Robustness for Free! CoRR (2022)
Florian Tramèr, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong, Nicholas Carlini
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets. CoRR (2022)
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, Chiyuan Zhang
Quantifying Memorization Across Neural Language Models. CoRR (2022)
Chawin Sitawarin, Florian Tramèr, Nicholas Carlini
Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems. CoRR (2022)
Ambra Demontis, Xinyun Chen, Florian Tramèr
AISec '22: 15th ACM Workshop on Artificial Intelligence and Security. CCS (2022)
Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, Florian Tramèr
The Privacy Onion Effect: Memorization is Relative. CoRR (2022)
Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini
Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy. CoRR (2022)
Florian Tramèr, Andreas Terzis, Thomas Steinke, Shuang Song, Matthew Jagielski, Nicholas Carlini
Debugging Differential Privacy: A Case Study for Privacy Auditing. CoRR (2022)
Florian Tramèr, Gautam Kamath, Nicholas Carlini
Considerations for Differentially Private Learning with Large-Scale Public Pretraining. CoRR (2022)
Roland S. Zimmermann, Wieland Brendel, Florian Tramèr, Nicholas Carlini
Increasing Confidence in Adversarial Robustness Evaluations. NeurIPS (2022)
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)
Florian Tramèr
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them. CoRR (2021)
Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr, Nicholas Carlini
Counterfactual Memorization in Neural Language Models. CoRR (2021)
Christopher A. Choquette-Choo, Florian Tramèr, Nicholas Carlini, Nicolas Papernot
Label-Only Membership Inference Attacks. ICML (2021)
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, Florian Tramèr
Membership Inference Attacks From First Principles. CoRR (2021)
Florian Tramèr, Dan Boneh
Differentially Private Learning Needs Better Features (or Much More Data). ICLR (2021)
Nicholas Carlini, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Florian Tramèr
NeuraCrypt is not private. CoRR (2021)
Mani Malek, Ilya Mironov, Karthik Prasad, Igor Shilov, Florian Tramèr
Antipodes of Label Differential Privacy: PATE and ALIBI. CoRR (2021)
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, Colin Raffel
Extracting Training Data from Large Language Models. USENIX Security Symposium (2021)
Xuechen Li, Florian Tramèr, Percy Liang, Tatsunori Hashimoto
Large Language Models Can Be Strong Differentially Private Learners. CoRR (2021)
Evani Radiya-Dixit, Florian Tramèr
Data Poisoning Won't Save You From Facial Recognition. CoRR (2021)
Mani Malek Esmaeili, Ilya Mironov, Karthik Prasad, Igor Shilov, Florian Tramèr
Antipodes of Label Differential Privacy: PATE and ALIBI. NeurIPS (2021)
Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Thakurta, Florian Tramèr
Is Private Learning Possible with Instance Encoding? IEEE Symposium on Security and Privacy (2021)
Charlie Hou, Mingxun Zhou, Yan Ji, Phil Daian, Florian Tramèr, Giulia Fanti, Ari Juels
SquirRL: Automating Attack Analysis on Blockchain Incentive Mechanisms with Deep Reinforcement Learning. NDSS (2021)
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14 (1-2) (2021)
Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. ICML (2020)
Christopher A. Choquette-Choo, Florian Tramèr, Nicholas Carlini, Nicolas Papernot
Label-Only Membership Inference Attacks. CoRR (2020)
Florian Tramèr, Nicholas Carlini, Wieland Brendel, Aleksander Madry
On Adaptive Attacks to Adversarial Example Defenses. NeurIPS (2020)
Florian Tramèr, Nicholas Carlini, Wieland Brendel, Aleksander Madry
On Adaptive Attacks to Adversarial Example Defenses. CoRR (2020)
Florian Tramèr, Dan Boneh
Differentially Private Learning Needs Better Features (or Much More Data). CoRR (2020)
Florian Tramèr, Dan Boneh, Kenneth G. Paterson
Remote Side-Channel Attacks on Anonymous Transactions. IACR Cryptol. ePrint Arch. 2020 (2020)
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)
Edward Chou, Florian Tramèr, Giancarlo Pellegrino
SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems. SP Workshops (2020)
Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. CoRR (2020)
Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Shuang Song, Abhradeep Thakurta, Florian Tramèr
An Attack on InstaHide: Is Private Learning Possible with Instance Encoding? CoRR (2020)
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, Colin Raffel
Extracting Training Data from Large Language Models. CoRR (2020)
Florian Tramèr, Dan Boneh, Kenny Paterson
Remote Side-Channel Attacks on Anonymous Transactions. USENIX Security Symposium (2020)
Florian Tramèr, Dan Boneh
Adversarial Training and Robustness for Multiple Perturbations. NeurIPS (2019)
Charlie Hou, Mingxun Zhou, Yan Ji, Phil Daian, Florian Tramèr, Giulia Fanti, Ari Juels
SquirRL: Automating Attack Discovery on Blockchain Incentive Mechanisms with Deep Reinforcement Learning. CoRR (2019)
Florian Tramèr, Dan Boneh
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware. ICLR (2019)
Lorenz Breidenbach, Philip Daian, Florian Tramèr, Ari Juels
The Hydra Framework for Principled, Automated Bug Bounties. IEEE Secur. Priv. 17 (4) (2019)
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
Advances and Open Problems in Federated Learning. CoRR (2019)
Florian Tramèr, Pascal Dupré, Gili Rusak, Giancarlo Pellegrino, Dan Boneh
AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning. CCS (2019)
Florian Tramèr, Dan Boneh
Adversarial Training and Robustness for Multiple Perturbations. CoRR (2019)
Jörn-Henrik Jacobsen, Jens Behrmann, Nicholas Carlini, Florian Tramèr, Nicolas Papernot
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness. CoRR (2019)
Florian Tramèr, Dan Boneh
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware. CoRR (2018)
Edward Chou, Florian Tramèr, Giancarlo Pellegrino, Dan Boneh
SentiNet: Detecting Physical Attacks Against Deep Learning Systems. CoRR (2018)
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, Patrick D. McDaniel
Ensemble Adversarial Training: Attacks and Defenses. ICLR (Poster) (2018)
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Florian Tramèr, Atul Prakash, Tadayoshi Kohno, Dawn Song
Physical Adversarial Examples for Object Detectors. CoRR (2018)

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Publication Activity (10 Years)

Top Topics

Top Venues

Florian Tramèr