{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T21:32:41Z","timestamp":1715895161649},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T00:00:00Z","timestamp":1663545600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T00:00:00Z","timestamp":1663545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s11063-022-11024-z","type":"journal-article","created":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T04:02:42Z","timestamp":1663560162000},"page":"3605-3625","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Generate Usable Adversarial Examples via Simulating Additional Light Sources"],"prefix":"10.1007","volume":"55","author":[{"given":"Chen","family":"Xi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guo","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhang","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Du","family":"Jiayu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,19]]},"reference":[{"issue":"3","key":"11024_CR1","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1109\/TIP.2004.838698","volume":"14","author":"RJ Radke","year":"2005","unstructured":"Radke RJ, Andra S, Al-Kofahi O, Roysam B (2005) Image change detection algorithms: a systematic survey. IEEE Trans Image Process 14(3):294\u2013307. https:\/\/doi.org\/10.1109\/TIP.2004.838698","journal-title":"IEEE Trans Image Process"},{"issue":"11","key":"11024_CR2","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324. https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc IEEE"},{"key":"11024_CR3","doi-asserted-by":"publisher","unstructured":"Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 815\u2013823. https:\/\/doi.org\/10.1109\/CVPR.2015.7298682","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"11024_CR4","doi-asserted-by":"crossref","unstructured":"Prakash A, Chitta K, Geiger A (2021) Multi-modal fusion transformer for end-to-end autonomous driving. In: 2021 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 7073\u20137083","DOI":"10.1109\/CVPR46437.2021.00700"},{"issue":"6","key":"11024_CR5","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun ACM"},{"key":"11024_CR6","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. CoRR abs\/1409.1556"},{"key":"11024_CR7","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"11024_CR8","unstructured":"Lehtinen J, Munkberg J, Hasselgren J, Laine S, Karras T, Aittala, M, Aila T (2018) Noise2noise: learning image restoration without clean data. arXiv:1803.04189"},{"key":"11024_CR9","unstructured":"Chen X, Liu C, Li B, Lu K, Song DX (2017) Targeted backdoor attacks on deep learning systems using data poisoning. arXiv:1712.05526"},{"key":"11024_CR10","unstructured":"Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow IJ, Fergus R (2014) Intriguing properties of neural networks. CoRR abs\/1312.6199"},{"key":"11024_CR11","doi-asserted-by":"publisher","first-page":"14410","DOI":"10.1109\/ACCESS.2018.2807385","volume":"6","author":"N Akhtar","year":"2018","unstructured":"Akhtar N, Mian A (2018) Threat of adversarial attacks on deep learning in computer vision: a survey. IEEE Access 6:14410\u201314430. https:\/\/doi.org\/10.1109\/ACCESS.2018.2807385","journal-title":"IEEE Access"},{"key":"11024_CR12","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2015) Explaining and harnessing adversarial examples. CoRR abs\/1412.6572"},{"key":"11024_CR13","doi-asserted-by":"crossref","unstructured":"Kurakin A, Goodfellow IJ, Bengio S (2017) Adversarial examples in the physical world. arXiv:1607.02533","DOI":"10.1201\/9781351251389-8"},{"key":"11024_CR14","doi-asserted-by":"crossref","unstructured":"Carlini N, Wagner DA (2017) Towards evaluating the robustness of neural networks. In: 2017 IEEE symposium on security and privacy (SP), pp 39\u201357","DOI":"10.1109\/SP.2017.49"},{"key":"11024_CR15","doi-asserted-by":"crossref","unstructured":"Papernot N, Mcdaniel P, Wu X, Jha S, Swami A (2016) Distillation as a defense to adversarial perturbations against deep neural networks. In: 2016 IEEE symposium on security and privacy (SP), pp 582\u2013597","DOI":"10.1109\/SP.2016.41"},{"key":"11024_CR16","doi-asserted-by":"publisher","unstructured":"Moosavi-Dezfooli SM, Fawzi A, Frossard P (2016) Deepfool: a simple and accurate method to fool deep neural networks. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 2574\u20132582 https:\/\/doi.org\/10.1109\/CVPR.2016.282","DOI":"10.1109\/CVPR.2016.282"},{"key":"11024_CR17","doi-asserted-by":"crossref","unstructured":"Papernot N, Mcdaniel P, Jha S, Fredrikson M, Celik ZB, Swami A (2016) The limitations of deep learning in adversarial settings. In: 2016 IEEE European symposium on security and privacy (EuroS &P), pp 372\u2013387","DOI":"10.1109\/EuroSP.2016.36"},{"key":"11024_CR18","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1109\/TEVC.2019.2890858","volume":"23","author":"J Su","year":"2019","unstructured":"Su J, Vargas DV, Sakurai K (2019) One pixel attack for fooling deep neural networks. IEEE Trans Evol Comput 23:828\u2013841","journal-title":"IEEE Trans Evol Comput"},{"key":"11024_CR19","unstructured":"Sarkar S, Bansal A, Mahbub U, Chellappa R (2017) Upset and angri : breaking high performance image classifiers. arXiv:1707.01159"},{"key":"11024_CR20","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Proceedings of the 27th international conference on neural information processing systems\u2014volume 2. NIPS\u201914. MIT Press, Cambridge, MA, pp 2672\u20132680"},{"key":"11024_CR21","doi-asserted-by":"crossref","unstructured":"Xiao C, Li B, Zhu JY, He W, Liu M, Song DX (2018) Generating adversarial examples with adversarial networks. arXiv:1801.02610","DOI":"10.24963\/ijcai.2018\/543"},{"key":"11024_CR22","unstructured":"Ilyas A, Engstrom L, Athalye A, Lin J (2018) Black-box adversarial attacks with limited queries and information. arXiv:1804.08598"},{"key":"11024_CR23","unstructured":"Uesat, J, O\u2019Donoghue B, van den Oord A, Kohli P (2018) Adversarial risk and the dangers of evaluating against weak attacks. arXiv:1802.05666"},{"key":"11024_CR24","doi-asserted-by":"publisher","unstructured":"Proch\u00e1zka S, Neruda R (2020) Black-box evolutionary search for adversarial examples against deep image classifiers in non-targeted attacks. In: 2020 international joint conference on neural networks (IJCNN), pp 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN48605.2020.9207688","DOI":"10.1109\/IJCNN48605.2020.9207688"},{"key":"11024_CR25","unstructured":"Brendel W, Rauber J, Bethge M (2018) Decision-based adversarial attacks: reliable attacks against black-box machine learning models. arXiv:1712.04248"},{"key":"11024_CR26","unstructured":"Li Y, Li L, Wang L, Zhang T, Gong B (2019) Nattack: learning the distributions of adversarial examples for an improved black-box attack on deep neural networks. arXiv:1905.00441"},{"key":"11024_CR27","unstructured":"Lu J, Sibai H, Fabry E, Forsyth DA (2017) No need to worry about adversarial examples in object detection in autonomous vehicles. arXiv:1707.03501"},{"key":"11024_CR28","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"11024_CR29","doi-asserted-by":"crossref","unstructured":"Zagoruyko S, Komodakis N (2016) Wide residual networks. arXiv:1605.07146","DOI":"10.5244\/C.30.87"},{"issue":"3","key":"11024_CR30","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1109\/JPROC.2020.2970615","volume":"108","author":"DJ Miller","year":"2020","unstructured":"Miller DJ, Xiang Z, Kesidis G (2020) Adversarial learning targeting deep neural network classification: a comprehensive review of defenses against attacks. Proc IEEE 108(3):402\u2013433. https:\/\/doi.org\/10.1109\/JPROC.2020.2970615","journal-title":"Proc IEEE"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-11024-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-022-11024-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-11024-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,8]],"date-time":"2023-07-08T12:16:30Z","timestamp":1688818590000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-022-11024-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,19]]},"references-count":30,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["11024"],"URL":"https:\/\/doi.org\/10.1007\/s11063-022-11024-z","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,19]]},"assertion":[{"value":"3 September 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}