Eluding ML-based Adblockers With Actionable Adversarial Examples-Reference-Cited by-同舟云学术

Eluding ML-based Adblockers With Actionable Adversarial Examples

Published:2021-12-06 Issue: Volume: Page:
ISSN:
Container-title:Annual Computer Security Applications Conference
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Author:

Zhu Shitong¹,Wang Zhongjie²,Chen Xun³,Li Shasha²,Man Keyu²,Iqbal Umar⁴,Qian Zhiyun²,Chan Kevin S.⁵,Krishnamurthy Srikanth V.²,Shafiq Zubair⁶,Hao Yu²,Li Guoren²,Zhang Zheng²,Zou Xiaochen²

Affiliation:

1. University of California, Riverside, United States of America

2. University of California, Riverside, USA

3. Samsung Research America, USA

4. University of Iowa, USA

5. US Army Research Laboratory, USA

6. University of California, Davis, USA

Funder

National Science Foundation

U.S. Army Combat Capabilities Development Command

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3485832.3488008

Reference34 articles.

1. Zainul Abi Din Panagiotis Tigas Samuel T King and Benjamin Livshits. 2020. {PERCIVAL}: Making in-browser perceptual ad blocking practical with deep learning. In 2020 {USENIX} Annual Technical Conference ({USENIX}{ATC} 20). 387–400. Zainul Abi Din Panagiotis Tigas Samuel T King and Benjamin Livshits. 2020. {PERCIVAL}: Making in-browser perceptual ad blocking practical with deep learning. In 2020 {USENIX} Annual Technical Conference ({USENIX}{ATC} 20). 387–400.

2. How Tracking Companies Circumvented Ad Blockers Using WebSockets

3. Leveraging Machine Learning to Improve Unwanted Resource Filtering

4. Joan Bruna Christian Szegedy Ilya Sutskever Ian Goodfellow Wojciech Zaremba Rob Fergus and Dumitru Erhan. 2013. Intriguing properties of neural networks. (2013). Joan Bruna Christian Szegedy Ilya Sutskever Ian Goodfellow Wojciech Zaremba Rob Fergus and Dumitru Erhan. 2013. Intriguing properties of neural networks. (2013).

5. François Chollet 2015. Keras. https://keras.io. François Chollet 2015. Keras. https://keras.io.

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