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The ability to transfer adversarial attacks from one model (the surrogate) to another model (the victim) has been an issue of concern within the machine learning (ML) community.
The painful face – Pain expression recognition using active appearance models
Ahmed Bilal Ashraf, Simon Lucey, Jeffrey F. Cohn, Tsuhan Chen, Zara Ambadar, Kenneth M. Prkachin, and Patricia E. Solomon · 2009
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Automatically detecting pain using facial actions
Patrick Lucey, Jeffrey Cohn, Simon Lucey, Iain Matthews, Sridha Sridharan, and Kenneth M. Prkachin · 2009
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Automatic detection of pain intensity
Zakia Hammal and Jeffrey F. Cohn · 2012
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Security evaluation of pattern classifiers under attack
Battista Biggio, Giorgio Fumera, and Fabio Roli · 2014
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Identifying malware genera using the Jensen-Shannon distance between system call traces
Jeremy D. Seideman, Bilal Khan, and Antonio Cesar Vargas · 2014
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Dropout : A Simple Way to Prevent Neural Networks from Overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Practical Evasion of a Learning-Based Classifier: A Case Study
Nedim Srndic and Pavel Laskov · 2014
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Adam: A Method for Stochastic Optimization
Diederik P Kingma and Jimmy Lei Ba · 2015
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Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow · 2016
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AVclass: A Tool for Massive Malware Labeling
Marcos Sebastián, Richard Rivera, Platon Kotzias, and Juan Caballero · 2016
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Machine Learning Methods Used in Evaluations of Secure Biometric System Components
Bilgehan Arslan, Mehtap Ulker, and Seref Sagiroglu · 2017
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Synthesizing Robust Adversarial Examples
Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok · 2017
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Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
Nicholas Carlini and David Wagner · 2017
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A Survey On Automated Dynamic Malware Analysis Evasion and Counter-Evasion
Manuel Egele, T Scholte, E Kirda, and Santa Barbara · 2017
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Adversarial Machine Learning at Scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2017
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Personalized Automatic Estimation of Self-Reported Pain Intensity from Facial Expressions
Daniel Lopez Martinez, Ognjen Rudovic, and Rosalind Picard · 2017
Cited alongside, same era.
Practical Black-Box Attacks against Machine Learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
Cited alongside, same era.
The Space of Transferable Adversarial Examples
Florian Tramèr, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
Cited alongside, same era.
EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models
Hyrum S. Anderson and Phil Roth · 2018
Cited alongside, same era.
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
Cited alongside, same era.
Wild patterns: Ten years after the rise of adversarial machine learning
Dynamics Are Important for the Recognition of Equine Pain in Video
Sofia Broome, Karina Bech Gleerup, Pia Haubro Andersen, and Hedvig Kjellstrom · 2019
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Improving black-box adversarial attacks with a transfer-based prior
Shuyu Cheng, Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu · 2019
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Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks
Ambra Demontis, Marco Melis, Maura Pintor, Matthew Jagielski, Battista Biggio, Alina Oprea, Cristina Nita-Rotaru, and Fabio Roli · 2019
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Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks
Y Dong, T Pang, H Su, and J Zhu · 2019
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Evading defenses to transferable adversarial examples by translation-invariant attacks
Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu · 2019
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Battista Biggio and Fabio Roli · 2018
Cited alongside, same era.
Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks
Ambra Demontis, Marco Melis, Maura Pintor, Matthew Jagielski, Battista Biggio, Alina Oprea, Cristina Nita-Rotaru, and Fabio Roli · 2018
Cited alongside, same era.
Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
Cited alongside, same era.
Adversarial Attacks Against Medical Deep Learning Systems
Samuel G Finlayson, Isaac S Kohane, and Andrew L Beam · 2018
Cited alongside, same era.
Adversarial malware binaries: Evading deep learning for malware detection in executables
Bojan Kolosnjaji, Ambra Demontis, Battista Biggio, Davide Maiorca, Giorgio Giacinto, Claudia Eckert, and Fabio Roli · 2018
Cited alongside, same era.
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Adversarial Attacks, Regression, and Numerical Stability Regularization
Andre T Nguyen and Edward Raff · 2018
Cited alongside, same era.
Slice: Scalable Linear Extreme Classifiers Trained on 100 Million Labels for Related Searches
Himanshu Jain, Venkatesh Balasubramanian, Bhanu Chunduri, and Manik Varma · 2019
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Improving Adversarial Robustness of Ensembles with Diversity Training
Sanjay Kariyappa and Moinuddin K. Qureshi · 2019
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Dr. AI, Where Did You Get Your Degree?
Edward Raff, Shannon Lantzy, and Ezekiel J Maier · 2019
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Improving Transferability of Adversarial Examples With Input Diversity
C Xie, Z Zhang, Y Zhou, S Bai, J Wang, Z Ren, and A L Yuille · 2019
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Taming Pretrained Transformers for Extreme Multi-Label Text Classification
Wei-Cheng Chang, Hsiang-Fu Yu, Kai Zhong, Yiming Yang, and Inderjit S Dhillon · 2020
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A Survey of Machine Learning Methods and Challenges for Windows Malware Classification
Edward Raff and Charles Nicholas · 2020
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Do Adversarially Robust ImageNet Models Transfer Better?
Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, and Aleksander Madry · 2020
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AVClass2: Massive Malware Tag Extraction from AV Labels
Silvia Sebastián and Juan Caballero · 2020
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Fawkes: Protecting Privacy against Unauthorized Deep Learning Models
Shawn Shan, Emily Wenger, Jiayun Zhang, Huiying Li, Haitao Zheng, and Ben Y. Zhao · 2020
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Fast is better than free: Revisiting adversarial training
Eric Wong, Leslie Rice, and J. Zico Kolter · 2020
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