Fetching the paper…
Reading the bibliography…
Achieving transferability of targeted attacks is reputed to be remarkably difficult.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Earlier work this paper cites.
Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2017
Earlier work this paper cites.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2017
Earlier work this paper cites.
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh · 2017
Earlier work this paper cites.
Adversarial image perturbation for privacy protection a game theory perspective
Seong Joon Oh, Mario Fritz, and Bernt Schiele · 2017
Earlier work this paper cites.
Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
Earlier work this paper cites.
Transferable adversarial perturbations
Wen Zhou, Xin Hou, Yongjun Chen, Mengyun Tang, Xiangqi Huang, Xiang Gan, and Yong Yang · 2018
Cited alongside, same era.
Generative adversarial perturbations
Omid Poursaeed, Isay Katsman, Bicheng Gao, and Serge Belongie · 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.
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.
Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2018
Cited alongside, same era.
Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Cross-domain transferability of adversarial perturbations
Muzammal Naseer, Salman H Khan, Harris Khan, Fahad Shahbaz Khan, and Fatih Porikli · 2019
Later among the works it cites.
On evaluating adversarial robustness
Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian Goodfellow, Aleksander Madry, and Alexey Kurakin · 2019
Later among the works it cites.
Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
Later among the works it cites.
Who’s afraid of adversarial queries? the impact of image modifications on content-based image retrieval
Zhuoran Liu, Zhengyu Zhao, and Martha Larson · 2019
Later among the works it cites.
Nesterov accelerated gradient and scale invariance for adversarial attacks
Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, and John E Hopcroft · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Wieland Brendel, Jonas Rauber, and Matthias Bethge · 2018
Cited alongside, same era.
Black-box adversarial attacks with limited queries and information
Andrew Ilyas, Logan Engstrom, Anish Athalye, and Jessy Lin · 2018
Cited alongside, same era.
Pixel privacy: Increasing image appeal while blocking automatic inference of sensitive scene information
Martha Larson, Zhuoran Liu, Simon Brugman, and Zhengyu Zhao · 2018
Cited alongside, same era.
Evading defenses to transferable adversarial examples by translation-invariant attacks
Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu · 2019
Cited alongside, same era.
Enhancing adversarial example transferability with an intermediate level attack
Qian Huang, Isay Katsman, Horace He, Zeqi Gu, Serge Belongie, and Ser-Nam Lim · 2019
Cited alongside, same era.
Improving transferability of adversarial examples with input diversity
Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai, Jianyu Wang, Zhou Ren, and Alan L Yuille · 2019
Cited alongside, same era.
Feature space perturbations yield more transferable adversarial examples
Nathan Inkawhich, Wei Wen, Hai Helen Li, and Yiran Chen · 2019
Cited alongside, same era.
Patch-wise attack for fooling deep neural network
Lianli Gao, Qilong Zhang, Jingkuan Song, Xianglong Liu, and Heng Tao Shen · 2020
Closest in time.
Understanding adversarial examples from the mutual influence of images and perturbations
Chaoning Zhang, Philipp Benz, Tooba Imtiaz, and In So Kweon · 2020
Closest in time.
Double targeted universal adversarial perturbations
Philipp Benz, Chaoning Zhang, Tooba Imtiaz, and In So Kweon · 2020
Closest in time.
On adaptive attacks to adversarial example defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel, and Aleksander Madry · 2020
Closest in time.
Fawkes: Protecting privacy against unauthorized deep learning models
Shawn Shan, Emily Wenger, Jiayun Zhang, Huiying Li, Haitao Zheng, and Ben Y Zhao · 2020
Closest in time.
On generating transferable targeted perturbations
Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli · 2021
Closest in time.
LowKey: Leveraging adversarial attacks to protect social media users from facial recognition
Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John Dickerson, Gavin Taylor, and Tom Goldstein · 2021
Closest in time.
On the (im) practicality of adversarial perturbation for image privacy
Arezoo Rajabi, Rakesh B Bobba, Mike Rosulek, Charles Wright, and Wu-chi Feng · 2021
Closest in time.