乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T02:36:13Z","timestamp":1720492573867},"reference-count":34,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB3102100"],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076187","62172303"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers & Security"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1016\/j.cose.2024.103791","type":"journal-article","created":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T16:28:40Z","timestamp":1709224120000},"page":"103791","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Model-agnostic adversarial example detection via high-frequency amplification"],"prefix":"10.1016","volume":"141","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-3694-8237","authenticated-orcid":false,"given":"Qiao","family":"Li","sequence":"first","affiliation":[]},{"given":"Jing","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Kun","family":"He","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0964-0034","authenticated-orcid":false,"given":"Zijun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ruiying","family":"Du","sequence":"additional","affiliation":[]},{"given":"Jisi","family":"She","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3785-9874","authenticated-orcid":false,"given":"Xinxin","family":"Wang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"11","key":"10.1016\/j.cose.2024.103791_br0010","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"Slic superpixels compared to state-of-the-art superpixel methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.cose.2024.103791_br0020","doi-asserted-by":"crossref","first-page":"14410","DOI":"10.1109\/ACCESS.2018.2807385","article-title":"Threat of adversarial attacks on deep learning in computer vision: a survey","volume":"6","author":"Akhtar","year":"2018","journal-title":"IEEE Access"},{"key":"10.1016\/j.cose.2024.103791_br0030","series-title":"2017 IEEE S&P","first-page":"39","article-title":"Towards evaluating the robustness of neural networks","author":"Carlini","year":"2017"},{"key":"10.1016\/j.cose.2024.103791_br0040","first-page":"6295","article-title":"Input-specific robustness certification for randomized smoothing","volume":"36","author":"Chen","year":"2022","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.cose.2024.103791_br0050","series-title":"Proceedings of the 1st ACM Workshop on Security and Privacy on Artificial Intelligence","first-page":"30","article-title":"Stateful detection of black-box adversarial attacks","author":"Chen","year":"2020"},{"key":"10.1016\/j.cose.2024.103791_br0060","series-title":"International Conference on Machine Learning","first-page":"2206","article-title":"Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks","author":"Croce","year":"2020"},{"key":"10.1016\/j.cose.2024.103791_br0070","author":"Dziugaite"},{"key":"10.1016\/j.cose.2024.103791_br0080","series-title":"Proceedings of the IEEE Conference on CVPR","first-page":"1625","article-title":"Robust physical-world attacks on deep learning visual classification","author":"Eykholt","year":"2018"},{"key":"10.1016\/j.cose.2024.103791_br0090","author":"Freitas"},{"key":"10.1016\/j.cose.2024.103791_br0100","series-title":"ICML","first-page":"2151","article-title":"Selectivenet: a deep neural network with an integrated reject option","author":"Geifman","year":"2019"},{"key":"10.1016\/j.cose.2024.103791_br0110","author":"Goodfellow"},{"key":"10.1016\/j.cose.2024.103791_br0120","author":"Hendrycks"},{"key":"10.1016\/j.cose.2024.103791_br0130","series-title":"Proceedings of the IEEE CVPR","first-page":"4700","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"key":"10.1016\/j.cose.2024.103791_br0140","series-title":"Adversarial Examples in the Physical World","author":"Kurakin","year":"2016"},{"key":"10.1016\/j.cose.2024.103791_br0150","article-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks","volume":"31","author":"Lee","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cose.2024.103791_br0160","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"5764","article-title":"Adversarial examples detection in deep networks with convolutional filter statistics","author":"Li","year":"2017"},{"key":"10.1016\/j.cose.2024.103791_br0170","series-title":"ICLR","article-title":"Towards deep learning models resistant to adversarial attacks","author":"Madry","year":"2018"},{"key":"10.1016\/j.cose.2024.103791_br0180","author":"Metzen"},{"key":"10.1016\/j.cose.2024.103791_br0190","series-title":"Proceedings of the IEEE Conference on CVPR","first-page":"2574","article-title":"Deepfool: a simple and accurate method to fool deep neural networks","author":"Moosavi-Dezfooli","year":"2016"},{"key":"10.1016\/j.cose.2024.103791_br0200","author":"Nicolae"},{"key":"10.1016\/j.cose.2024.103791_br0210","author":"Papernot"},{"key":"10.1016\/j.cose.2024.103791_br0220","series-title":"2016 IEEE S&P","first-page":"582","article-title":"Distillation as a defense to adversarial perturbations against deep neural networks","author":"Papernot","year":"2016"},{"key":"10.1016\/j.cose.2024.103791_br0230","series-title":"Proceedings of the IEEE\/CVF CVPR","first-page":"6528","article-title":"Barrage of random transforms for adversarially robust defense","author":"Raff","year":"2019"},{"key":"10.1016\/j.cose.2024.103791_br0240","series-title":"Thirty-Second AAAI Conference on Artificial Intelligence","article-title":"Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients","author":"Ross","year":"2018"},{"key":"10.1016\/j.cose.2024.103791_br0250","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.neucom.2018.04.027","article-title":"Understanding adversarial training: increasing local stability of supervised models through robust optimization","volume":"307","author":"Shaham","year":"2018","journal-title":"Neurocomputing"},{"key":"10.1016\/j.cose.2024.103791_br0260","series-title":"Proceedings of ECCV","first-page":"631","article-title":"Is robustness the cost of accuracy?\u2013a comprehensive study on the robustness of 18 deep image classification models","author":"Su","year":"2018"},{"key":"10.1016\/j.cose.2024.103791_br0270","series-title":"Detecting Adversarial Examples Using Image Reconstruction Differences, vol. 27","first-page":"7863","author":"Sun","year":"2023"},{"key":"10.1016\/j.cose.2024.103791_br0280","author":"Szegedy"},{"key":"10.1016\/j.cose.2024.103791_br0290","first-page":"9877","article-title":"Detecting adversarial examples from sensitivity inconsistency of spatial-transform domain","volume":"35","author":"Tian","year":"2021","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.cose.2024.103791_br0300","series-title":"Thirty-Second AAAI Conference on Artificial Intelligence","article-title":"Detecting adversarial examples through image transformation","author":"Tian","year":"2018"},{"key":"10.1016\/j.cose.2024.103791_br0310","series-title":"ICLR, vol. 2019","article-title":"Robustness may be at odds with accuracy","author":"Tsipras","year":"2019"},{"key":"10.1016\/j.cose.2024.103791_br0320","author":"Xu"},{"key":"10.1016\/j.cose.2024.103791_br0330","series-title":"ICML","article-title":"Me-net: towards effective adversarial robustness with matrix estimation","author":"Yang","year":"2019"},{"key":"10.1016\/j.cose.2024.103791_br0340","series-title":"Proceedings of the 10th ACM Workshop on AISE","first-page":"39","article-title":"Efficient defenses against adversarial attacks","author":"Zantedeschi","year":"2017"}],"updated-by":[{"updated":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"DOI":"10.1016\/j.cose.2024.104006","type":"erratum","label":"Erratum"}],"container-title":["Computers & Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167404824000920?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167404824000920?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T18:52:41Z","timestamp":1714071161000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167404824000920"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":34,"alternative-id":["S0167404824000920"],"URL":"https:\/\/doi.org\/10.1016\/j.cose.2024.103791","relation":{},"ISSN":["0167-4048"],"issn-type":[{"value":"0167-4048","type":"print"}],"subject":[],"published":{"date-parts":[[2024,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Model-agnostic adversarial example detection via high-frequency amplification","name":"articletitle","label":"Article Title"},{"value":"Computers & Security","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cose.2024.103791","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"103791"}}