{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T10:58:14Z","timestamp":1761562694611,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030591519"},{"type":"electronic","value":"9783030591526"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-59152-6_5","type":"book-chapter","created":{"date-parts":[[2020,10,11]],"date-time":"2020-10-11T23:02:35Z","timestamp":1602457355000},"page":"92-107","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["DeepAbstract: Neural Network Abstraction for Accelerating Verification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1083-4741","authenticated-orcid":false,"given":"Pranav","family":"Ashok","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9167-7417","authenticated-orcid":false,"given":"Vahid","family":"Hashemi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8122-2881","authenticated-orcid":false,"given":"Jan","family":"K\u0159et\u00ednsk\u00fd","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8630-3218","authenticated-orcid":false,"given":"Stefanie","family":"Mohr","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,12]]},"reference":[{"key":"5_CR1","unstructured":"Abadi, M., et al.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org. (2015). \nhttps:\/\/www.tensorflow.org\/"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Akhtar, N., Mian, A.: Threat of adversarial attacks on deep learning in computer vision: a survey. In: IEEE Access, vol. 6, pp. 14410\u201314430 (2018)","DOI":"10.1109\/ACCESS.2018.2807385"},{"key":"5_CR3","unstructured":"Ashok, P., et al.: DeepAbstract: neural network abstraction for accelerating verification. Technical report (2020). \narXiv: 2006.13735\n\n [cs.LO]"},{"key":"5_CR4","unstructured":"Christopher M Bishop. Pattern recognition and machine learning. springer, 2006"},{"key":"5_CR5","unstructured":"Clarke, E.M., Grumberg, O., Long, D.E.: Model checking and abstraction. ACM Trans. Program. Lang. Syst. 16(5), 1512\u20131542 (1994)"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Chen, X., et al.: Multi-view 3d object detection network for autonomous driving. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.691"},{"key":"5_CR7","unstructured":"Yu, C., et al.: A Survey of Model Compression and Acceleration for Deep Neural Networks. In: CoRR abs\/1710.09282 (2017)"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Clarke, E.M., et al.: Counterexample-guided abstraction refinement. In: CAV (2000)","DOI":"10.1007\/10722167_15"},{"key":"5_CR9","unstructured":"Chih-Hong, C., N\u00fchrenberg, G., Ruess, H.: Maximum resilience of artificial neural networks. In: ATVA (2017)"},{"key":"5_CR10","unstructured":"Lei, D., et al.: Model compression and hardware acceleration for neural networks: a comprehensive survey. In: Proceedings of the IEEE 108(4), 485\u2013532 (2020)"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Dong, Y., et al.: Boosting adversarial attacks with momentum. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00957"},{"key":"5_CR12","unstructured":"Krishnamurthy, D., et al.: A dual approach to scalable verification of deep networks. In: UAI (2018)"},{"key":"5_CR13","unstructured":"R\u00fcdiger, E.: Formal verification of piece-wise linear feed- forward neural networks. In: ATVA (2017)"},{"key":"5_CR14","unstructured":"Timon, G., et al.: Ai2: safety and robustness certification of neural networks with abstract interpretation. In: 2018 IEEE Symposium on Security and Privacy (SP) (2018)"},{"key":"5_CR15","unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural network with pruning, trained quantization and huffman coding. In: ICLR (2016)"},{"key":"5_CR16","unstructured":"Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, 2009"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Huang, X., et al.: Safety verification of deep neural networks. In: CAV, no. 1 (2017)","DOI":"10.1007\/978-3-319-63387-9_1"},{"key":"5_CR18","unstructured":"Julian, K.D., Kochenderfer, M.J., Owen, M.P.: Deep Neural Network Compression for Aircraft Collision Avoidance Systems. In: CoRR abs\/1810.04240 (2018)"},{"key":"5_CR19","unstructured":"Guy, K., et al.: Reluplex: an efficient SMT solver for verifying deep neural networks. In: CAV, no. 1 (2017)"},{"key":"5_CR20","unstructured":"LeCun, Y.: The MNIST database of handwritten digits. \nhttp:\/\/yann.lecun.com\/exdb\/mnist\/\n\n (1998)"},{"key":"5_CR21","unstructured":"Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: ICML (2013)"},{"key":"5_CR22","unstructured":"Pavithra, P., Zahra, R.A.: Abstraction based output range analysis for neural networks. In: NeurIPS (2019)"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Papernot, N., et al.: The limitations of deep learning in adversarial settings. In: EuroS&P. IEEE (2016)","DOI":"10.1109\/EuroSP.2016.36"},{"key":"5_CR24","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine Learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)"},{"key":"5_CR25","unstructured":"Luca, P., Armando, T.: An abstraction-refinement approach to verification of artificial neural networks. In: CAV (2010)"},{"key":"5_CR26","unstructured":"Suraj, S., Venkatesh Babu, R.: Data-free parameter pruning for deep neural networks. In: BMVC (2015)"},{"key":"5_CR27","doi-asserted-by":"crossref","unstructured":"Singh, G., et al.: An abstract domain for certifying neural networks. In: Proceedings ACM Program. Lang. vol. 3.POPL, 41:1\u201341:30 (2019)","DOI":"10.1145\/3290354"},{"key":"5_CR28","unstructured":"Singh, G., et al.: Boosting robustness certification of neural networks. In: ICLR (Poster) (2019)"},{"key":"5_CR29","unstructured":"Su, J., Vasconcellos Vargas, D., Sakurai, K.: One pixel attack for fooling deep neural networks. IEEE Trans. Evol. Comput. 23(5), 828\u2013841 (2019)"},{"key":"5_CR30","doi-asserted-by":"crossref","unstructured":"Elboher, Y.Y., Gottschlich, J., Katz, G.: An abstraction-based framework for neural network verification. In: arXiv e-prints, \narXiv:1910.14574\n\n (2019)","DOI":"10.1007\/978-3-030-53288-8_3"},{"key":"5_CR31","doi-asserted-by":"crossref","unstructured":"Zhong, G., Yao, H., Zhou, H.: Merging neurons for structure compression of deep networks. In: ICPR (2018)","DOI":"10.1109\/ICPR.2018.8545107"}],"container-title":["Lecture Notes in Computer Science","Automated Technology for Verification and Analysis"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59152-6_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,11]],"date-time":"2020-10-11T23:04:54Z","timestamp":1602457494000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-59152-6_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030591519","9783030591526"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59152-6_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"12 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ATVA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Automated Technology for Verification and Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"atva2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/fit.uet.vnu.edu.vn\/atva2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"75","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}