{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T00:08:45Z","timestamp":1755907725608,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T00:00:00Z","timestamp":1638748800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1652954,2051592,2103439,2102347,1719147"],"award-info":[{"award-number":["1652954,2051592,2103439,2102347,1719147"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014819","name":"U.S. Army Combat Capabilities Development Command","doi-asserted-by":"publisher","award":["W911NF-13-2-0045"],"award-info":[{"award-number":["W911NF-13-2-0045"]}],"id":[{"id":"10.13039\/100014819","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,12,6]]},"DOI":"10.1145\/3485832.3488008","type":"proceedings-article","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T13:42:32Z","timestamp":1638798152000},"page":"541-553","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Eluding ML-based Adblockers With Actionable Adversarial Examples"],"prefix":"10.1145","author":[{"given":"Shitong","family":"Zhu","sequence":"first","affiliation":[{"name":"University of California, Riverside, United States of America"}]},{"given":"Zhongjie","family":"Wang","sequence":"additional","affiliation":[{"name":"University of California, Riverside, USA"}]},{"given":"Xun","family":"Chen","sequence":"additional","affiliation":[{"name":"Samsung Research America, USA"}]},{"given":"Shasha","family":"Li","sequence":"additional","affiliation":[{"name":"University of California, Riverside, USA"}]},{"given":"Keyu","family":"Man","sequence":"additional","affiliation":[{"name":"University of California, Riverside, USA"}]},{"given":"Umar","family":"Iqbal","sequence":"additional","affiliation":[{"name":"University of Iowa, USA"}]},{"given":"Zhiyun","family":"Qian","sequence":"additional","affiliation":[{"name":"University of California, Riverside, USA"}]},{"given":"Kevin S.","family":"Chan","sequence":"additional","affiliation":[{"name":"US Army Research Laboratory, USA"}]},{"given":"Srikanth V.","family":"Krishnamurthy","sequence":"additional","affiliation":[{"name":"University of California, Riverside, USA"}]},{"given":"Zubair","family":"Shafiq","sequence":"additional","affiliation":[{"name":"University of California, Davis, USA"}]},{"given":"Yu","family":"Hao","sequence":"additional","affiliation":[{"name":"University of California, Riverside, USA"}]},{"given":"Guoren","family":"Li","sequence":"additional","affiliation":[{"name":"University of California, Riverside, USA"}]},{"given":"Zheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of California, Riverside, USA"}]},{"given":"Xiaochen","family":"Zou","sequence":"additional","affiliation":[{"name":"University of California, Riverside, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,12,6]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Zainul Abi\u00a0Din Panagiotis Tigas Samuel\u00a0T 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\u2013400."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3278532.3278573"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2666652.2666662"},{"key":"e_1_3_2_1_4_1","unstructured":"Joan Bruna Christian Szegedy Ilya Sutskever Ian Goodfellow Wojciech Zaremba Rob Fergus and Dumitru Erhan. 2013. Intriguing properties of neural networks. (2013)."},{"key":"e_1_3_2_1_5_1","unstructured":"Fran\u00e7ois Chollet 2015. Keras. https:\/\/keras.io."},{"key":"e_1_3_2_1_6_1","unstructured":"Catalin Cimpanu. 2018. Ad Network Uses DGA Algorithm to Bypass Ad Blockers and Deploy In-Browser Miners. https:\/\/www.bleepingcomputer.com\/news\/security\/ad-network-uses-dga-algorithm-to-bypass-ad-blockers-and-deploy-in-browser-miners\/."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00957"},{"key":"e_1_3_2_1_8_1","volume-title":"International Conference on Machine Learning. PMLR, 2280\u20132289","author":"Gilmer Justin","year":"2019","unstructured":"Justin Gilmer, Nicolas Ford, Nicholas Carlini, and Ekin Cubuk. 2019. Adversarial examples are a natural consequence of test error in noise. In International Conference on Machine Learning. PMLR, 2280\u20132289."},{"key":"e_1_3_2_1_9_1","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Hendrycks Dan","year":"2019","unstructured":"Dan Hendrycks and Thomas Dietterich. 2019. Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. Proceedings of the International Conference on Learning Representations (2019)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3131365.3131387"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP40000.2020.00005"},{"key":"e_1_3_2_1_12_1","unstructured":"Vishveshwar Jatain. 2020. Countering the revenue loss caused by ad blockers. https:\/\/digitalcontentnext.org\/blog\/2020\/08\/12\/countering-the-revenue-loss-caused-by-ad-blockers\/."},{"volume-title":"Adversarial malware binaries: Evading deep learning for malware detection in executables. In 2018 26th European signal processing conference (EUSIPCO)","author":"Kolosnjaji Bojan","key":"e_1_3_2_1_13_1","unstructured":"Bojan Kolosnjaji, Ambra Demontis, Battista Biggio, Davide Maiorca, Giorgio Giacinto, Claudia Eckert, and Fabio Roli. 2018. Adversarial malware binaries: Evading deep learning for malware detection in executables. In 2018 26th European signal processing conference (EUSIPCO). IEEE, 533\u2013537."},{"key":"e_1_3_2_1_14_1","unstructured":"Alexey Kurakin Ian Goodfellow and Samy Bengio. 2016. Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236(2016)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24055"},{"key":"e_1_3_2_1_16_1","volume-title":"Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistency. In European Conference on Computer Vision. Springer, 396\u2013413","author":"Li Shasha","year":"2020","unstructured":"Shasha Li, Shitong Zhu, Sudipta Paul, Amit Roy-Chowdhury, Chengyu Song, Srikanth Krishnamurthy, Ananthram Swami, and Kevin\u00a0S Chan. 2020. Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistency. In European Conference on Computer Vision. Springer, 396\u2013413."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1515\/popets-2017-0032"},{"key":"e_1_3_2_1_18_1","unstructured":"PageFair. 2017. 2017 Global Adblock Report. PageFair. https:\/\/pagefair.com\/downloads\/2017\/01\/PageFair-2017-Adblock-Report.pdf."},{"key":"e_1_3_2_1_19_1","unstructured":"Nicolas Papernot Patrick McDaniel and Ian Goodfellow. 2016. Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. arXiv preprint arXiv:1605.07277(2016)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00159"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP40000.2020.00073"},{"key":"e_1_3_2_1_22_1","volume-title":"Induction of decision trees. Machine learning 1, 1","author":"Quinlan Ross","year":"1986","unstructured":"J.\u00a0Ross Quinlan. 1986. Induction of decision trees. Machine learning 1, 1 (1986), 81\u2013106."},{"key":"e_1_3_2_1_23_1","volume-title":"Foolbox: A python toolbox to benchmark the robustness of machine learning models. arXiv preprint arXiv:1707.04131(2017).","author":"Rauber Jonas","year":"2017","unstructured":"Jonas Rauber, Wieland Brendel, and Matthias Bethge. 2017. Foolbox: A python toolbox to benchmark the robustness of machine learning models. arXiv preprint arXiv:1707.04131(2017)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.21105\/joss.02607"},{"key":"e_1_3_2_1_25_1","volume-title":"Beautiful soup documentation. April","author":"Richardson Leonard","year":"2007","unstructured":"Leonard Richardson. 2007. Beautiful soup documentation. April (2007)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380239"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Grant Storey Dillon Reisman Jonathan Mayer and Arvind Narayanan. 2017. The future of ad blocking: An analytical framework and new techniques. arXiv preprint arXiv:1705.08568(2017).","DOI":"10.1063\/pt.5.9095"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3319535.3354222"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380203"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2950290.2950352"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3428230"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5469"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2018.23331"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3386367.3431311"}],"event":{"name":"ACSAC '21: Annual Computer Security Applications Conference","acronym":"ACSAC '21","location":"Virtual Event USA"},"container-title":["Annual Computer Security Applications Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485832.3488008","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3485832.3488008","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3485832.3488008","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T19:18:55Z","timestamp":1755890335000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485832.3488008"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,6]]},"references-count":34,"alternative-id":["10.1145\/3485832.3488008","10.1145\/3485832"],"URL":"https:\/\/doi.org\/10.1145\/3485832.3488008","relation":{},"subject":[],"published":{"date-parts":[[2021,12,6]]},"assertion":[{"value":"2021-12-06","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}