{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T12:29:56Z","timestamp":1765369796159,"version":"3.37.3"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T00:00:00Z","timestamp":1618185600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T00:00:00Z","timestamp":1618185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,10]]},"DOI":"10.1007\/s00521-021-05957-0","type":"journal-article","created":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T00:02:40Z","timestamp":1618185760000},"page":"13333-13343","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Semi-supervised deep learning approach to break common CAPTCHAs"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7856-2084","authenticated-orcid":false,"given":"Ondrej","family":"Bostik","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2280-3029","authenticated-orcid":false,"given":"Karel","family":"Horak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8425-323X","authenticated-orcid":false,"given":"Lukas","family":"Kratochvila","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4363-4313","authenticated-orcid":false,"given":"Tomas","family":"Zemcik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8797-7700","authenticated-orcid":false,"given":"Simon","family":"Bilik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,4,12]]},"reference":[{"key":"5957_CR1","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1007\/3-540-39200-9_18","volume-title":"Lecture Notes in Computer Science","author":"L von Ahn","year":"2003","unstructured":"von Ahn L, Blum M, Hopper NJ, Langford J (2003) CAPTCHA: Using Hard AI Problems for Security. Lecture Notes in Computer Science. Springer, Berlin, pp 294\u2013311. https:\/\/doi.org\/10.1007\/3-540-39200-9_18"},{"key":"5957_CR2","doi-asserted-by":"publisher","unstructured":"Arai T, Okabe Y, Matsumoto Y (2021) Precursory analysis of attack-log time series by machine learning for detecting bots in CAPTCHA. In: 2021 International Conference on information networking (ICOIN), pp. 295\u2013300 https:\/\/doi.org\/10.1109\/ICOIN50884.2021.9333881","DOI":"10.1109\/ICOIN50884.2021.9333881"},{"key":"5957_CR3","doi-asserted-by":"publisher","unstructured":"Arai T, Okabe Y, Matsumoto Y, Kawamura, K (2020) Detection of Bots in CAPTCHA as a cloud service utilizing machine learning. In: 2020 International conference on information networking (ICOIN), pp. 584\u2013589 https:\/\/doi.org\/10.1109\/ICOIN48656.2020.9016522","DOI":"10.1109\/ICOIN48656.2020.9016522"},{"key":"5957_CR4","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/11909033_9","volume":"4237","author":"E Athanasopoulos","year":"2006","unstructured":"Athanasopoulos E, Antonatos S (2006) Enhanced CAPTCHAs: using animation to tell humans and computers apart. Ifip Int Federation Information Process 4237:97\u2013108. https:\/\/doi.org\/10.1007\/11909033_9","journal-title":"Ifip Int Federation Information Process"},{"key":"5957_CR5","unstructured":"Bursztein E, Aigrain J, Moscicki A, Mitchell JC (2014) The end is nigh: generic solving of text-based CAPTCHAs http:\/\/portal.acm.org\/citation.cfm?id=2671296"},{"key":"5957_CR6","doi-asserted-by":"publisher","unstructured":"Bursztein E, Beauxis R, Paskov H, Perito D, Fabry C, Mitchell J (2011)The failure of noise-based non-continuous audio captchas. In: Proceedings - IEEE symposium on security and privacy, pp. 19\u201331 https:\/\/doi.org\/10.1109\/SP.2011.14","DOI":"10.1109\/SP.2011.14"},{"key":"5957_CR7","unstructured":"Bursztein E, Bethard S (2009) Decaptcha: breaking 75% of eBay audio CAPTCHAs. Proceedings of the 3rd USENIX conference on Offensive technologies 1(8), 1\u20137"},{"key":"5957_CR8","doi-asserted-by":"publisher","unstructured":"Bursztein E, Bethard S, Fabry C, Mitchell JC, Jurafsky D (2010) How good are humans at solving CAPTCHAs? a large scale evaluation. In: Proceedings - IEEE symposium on security and privacy. pp. 399\u2013413. IEEE . https:\/\/doi.org\/10.1109\/SP.2010.31. http:\/\/ieeexplore.ieee.org\/document\/5504799\/","DOI":"10.1109\/SP.2010.31"},{"key":"5957_CR9","doi-asserted-by":"publisher","unstructured":"Bursztein E, Martin M, Mitchell JC (2011) Text-based CAPTCHA strengths and weaknesses. In: proceedings of the ACM conference on computer and communications security, pp. 125\u2013138 . https:\/\/doi.org\/10.1145\/2046707.2046724","DOI":"10.1145\/2046707.2046724"},{"key":"5957_CR10","doi-asserted-by":"crossref","unstructured":"Bursztein E, Moscicki A, Fabry C, Bethard S, Mitchell JC, Jurafasky D (2014) Easy Does It: more usable CAPTCHAs. In: CHI \u201914 proceedings of the SIGCHI conference on human factors in computing systems. pp. 2637\u20132646. 1600 Amphitheatre Pkwy https:\/\/www.elie.net\/publication\/easy-does-it-more-usable-captchas","DOI":"10.1145\/2556288.2557322"},{"key":"5957_CR11","doi-asserted-by":"crossref","unstructured":"Chellapilla K, Larson K, Simard P, Czerwinski M (2005) Computers beat humans at single character recognition in reading based human interaction proofs (HIPs). In: 2nd Conference on Email and Anti-Spam, pp. 1\u20138. Conference on Email and Anti-Spam, CEAS","DOI":"10.1007\/11427896_1"},{"key":"5957_CR12","unstructured":"Chellapilla K, Simard P (2005) Using machine learning to break visual human interaction proofs (HIPs). In: Saul L, Weiss Y, Bottou L (eds.) Advances in neural information processing systems, vol 17. MIT Press, Vancouver, pp 265\u2013272.  https:\/\/proceedings.neurips.cc\/paper\/2004\/file\/283085d30e10513624c8cece7993f4de-Paper.pdf"},{"key":"5957_CR13","doi-asserted-by":"publisher","unstructured":"Chow YW, Susilo W (2011) AniCAP: An Animated 3D CAPTCHA scheme based on motion parallax. In: D.\u00a0Lin, G.\u00a0Tsudik, X.\u00a0Wang (eds.) Cryptology and network security: 10th International conference, CANS 2011, Sanya, China, December 10-12, 2011. Proceedings, pp. 255\u2013271. Springer Berlin Heidelberg, Berlin, Heidelberg . https:\/\/doi.org\/10.1007\/978-3-642-25513-7_18","DOI":"10.1007\/978-3-642-25513-7_18"},{"key":"5957_CR14","doi-asserted-by":"publisher","unstructured":"Desai A, Patadia P (2009) Drag and drop: a better approach to CAPTCHA. In: 2009 Annual IEEE India Conference, pp. 1\u20134 . https:\/\/doi.org\/10.1109\/INDCON.2009.5409359","DOI":"10.1109\/INDCON.2009.5409359"},{"issue":"1","key":"5957_CR15","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119\u2013139. https:\/\/doi.org\/10.1006\/jcss.1997.1504","journal-title":"J Comput Syst Sci"},{"issue":"7","key":"5957_CR16","doi-asserted-by":"publisher","first-page":"1671","DOI":"10.1109\/TIFS.2017.2682704","volume":"12","author":"H Gao","year":"2017","unstructured":"Gao H, Tang M, Liu Y, Zhang P, Liu X (2017) Research on the security of microsoft\u2019s two-layer captcha. IEEE Transactions Information Forensics Secur 12(7):1671\u20131685. https:\/\/doi.org\/10.1109\/TIFS.2017.2682704","journal-title":"IEEE Transactions Information Forensics Secur"},{"key":"5957_CR17","doi-asserted-by":"publisher","unstructured":"Gao H, Wang W, Qi J, Wang X, Liu X, Yan J (2013) The robustness of hollow CAPTCHAs. In: Proceedings of the ACM conference on computer and communications security. pp 1075\u20131086 . https:\/\/doi.org\/10.1145\/2508859.2516732","DOI":"10.1145\/2508859.2516732"},{"key":"5957_CR18","doi-asserted-by":"publisher","unstructured":"Gao H, Yan J, Cao F, Zhang Z, Lei L, Tang M, Zhang P, Zhou X, Wang X, Li J (2016) A simple generic attack on text captchas. In: Network and distributed system security symposium (NDSS 2016), pp. 1\u201326. https:\/\/doi.org\/10.14722\/ndss.2016.23154","DOI":"10.14722\/ndss.2016.23154"},{"key":"5957_CR19","doi-asserted-by":"publisher","unstructured":"Horak K, Sablatnig R (2019) Deep learning concepts and datasets for image recognition: overview 2019. In: Eleventh international conference on digital image processing (ICDIP 2019), 11179, pp 484\u2013491. SPIE . https:\/\/doi.org\/10.1117\/12.2539806","DOI":"10.1117\/12.2539806"},{"key":"5957_CR20","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.procs.2015.07.381","volume":"57","author":"K Kaur","year":"2015","unstructured":"Kaur K, Behal S (2015) Designing a secure text-based CAPTCHA. Procedia Comput Sci 57:122\u2013125. https:\/\/doi.org\/10.1016\/j.procs.2015.07.381","journal-title":"Procedia Comput Sci"},{"key":"5957_CR21","doi-asserted-by":"crossref","unstructured":"Kiselak J, Lu Y, Svihra J, Szepe P, Stehlik M (2021) \u201cSPOCU\u201d: scaled polynomial constant unit activation function. Neural Comput Appl 33:3385\u20133401","DOI":"10.1007\/s00521-020-05182-1"},{"key":"5957_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05412-6","author":"J Kisel\u2019\u00e1k","year":"2020","unstructured":"Kisel\u2019\u00e1k J, Lu Y, \u0160vihra J, Sz\u00e9pe P, Stehl\u00edk M (2020) Correction to: SPOCU: scaled polynomial constant unit activation function. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-020-05412-6","journal-title":"Neural Comput Appl"},{"key":"5957_CR23","unstructured":"Klambauer G, Unterthiner T, Mayr A, Hochreiter S (2017) Self-Normalizing neural networks. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds.) Advances in neural information processing systems 30 (NIPS 2017), vol 30. Curran Associates, Inc., pp 971\u2013980. https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/5d44ee6f2c3f71b73125876103c8f6c4-Paper.pdf"},{"key":"5957_CR24","unstructured":"Mori G, Malik J (2003) Recognizing objects in adversarial clutter: breaking a visual CAPTCHA. 2003 IEEE computer society conference on computer vision and pattern recognition, 2003. Proceedings. 1, I\u2013I"},{"key":"5957_CR25","unstructured":"Murphy KP (2012) Machine learning: a probabilistic perspective, 1, edition. The MIT Press, Cambridge, MA"},{"key":"5957_CR26","unstructured":"Nair V, Hinton G (2010) Rectified linear units improve restricted boltzmann machines Vinod Nair. Proceedings of ICML 27:807\u2013814"},{"key":"5957_CR27","doi-asserted-by":"publisher","unstructured":"Nguyen VD, Chow YW, Susilo W (2014) A CAPTCHA scheme based on the identification of character locations. In: X.\u00a0Huang, J.\u00a0Zhou (eds.) Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol. 8434 LNCS, pp. 60\u201374. Springer, Cham . https:\/\/doi.org\/10.1007\/978-3-319-06320-1_6","DOI":"10.1007\/978-3-319-06320-1_6"},{"key":"5957_CR28","doi-asserted-by":"crossref","unstructured":"Noury Z, Rezaei M (2020) Deep-CAPTCHA: a deep learning based CAPTCHA solver for vulnerability assessment . arXiv:2006.08296","DOI":"10.31219\/osf.io\/km35b"},{"key":"5957_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.09.006","author":"O Starostenko","year":"2015","unstructured":"Starostenko O, Cruz-Perez C, Uceda-Ponga F, Alarcon-Aquino V (2015) Breaking text-based CAPTCHAs with variable word and character orientation. Pattern Recognit. https:\/\/doi.org\/10.1016\/j.patcog.2014.09.006","journal-title":"Pattern Recognit"},{"issue":"10","key":"5957_CR30","doi-asserted-by":"publisher","first-page":"2522","DOI":"10.1109\/TIFS.2018.2821096","volume":"13","author":"M Tang","year":"2018","unstructured":"Tang M, Gao H, Zhang Y, Liu Y, Zhang P, Wang P (2018) Research on deep learning techniques in breaking text-based captchas and designing image-based captcha. IEEE Transactions Information Forensics Secur 13(10):2522\u20132537. https:\/\/doi.org\/10.1109\/TIFS.2018.2821096","journal-title":"IEEE Transactions Information Forensics Secur"},{"key":"5957_CR31","doi-asserted-by":"publisher","first-page":"59044","DOI":"10.1109\/ACCESS.2020.2982945","volume":"8","author":"P Wang","year":"2020","unstructured":"Wang P, Gao H, Shi Z, Yuan Z, Hu J (2020) Simple and easy: transfer learning-based attacks to text CAPTCHA. IEEE Access 8:59044\u201359058. https:\/\/doi.org\/10.1109\/ACCESS.2020.2982945","journal-title":"IEEE Access"},{"key":"5957_CR32","doi-asserted-by":"publisher","unstructured":"Yan J, Ahmad ASE (2008) Breaking visual CAPTCHAs with naive pattern recognition algorithms. In: Twenty-Third annual computer security applications conference (ACSAC 2007), pp. 279\u2013297 . https:\/\/doi.org\/10.1109\/acsac.2007.4412996","DOI":"10.1109\/acsac.2007.4412996"},{"key":"5957_CR33","unstructured":"Yang H (2020) GitHub - lepture\/captcha: A CAPTCHA library that generates audio and image CAPTCHAs. . https:\/\/github.com\/lepture\/captcha\/"},{"key":"5957_CR34","doi-asserted-by":"publisher","unstructured":"Ye G, Tang Z, Fang D, Zhu Z, Feng Y, Xu P, Chen X, Wang Z (2018) Yet another text captcha solver: a generative adversarial network based approach. In: Proceedings of the 2018 ACM SIGSAC conference on computer and communications security, CCS \u201918, pp. 332\u2013348. Association for computing machinery, New York, NY, USA . https:\/\/doi.org\/10.1145\/3243734.3243754","DOI":"10.1145\/3243734.3243754"},{"key":"5957_CR35","doi-asserted-by":"publisher","unstructured":"Zhang N, Ebrahimi M, Li W, Chen H (2020) A generative adversarial learning framework for breaking text-based CAPTCHA in the dark web. In: 2020 IEEE International conference on intelligence and security informatics (ISI), pp. 1\u20136 . https:\/\/doi.org\/10.1109\/ISI49825.2020.9280537","DOI":"10.1109\/ISI49825.2020.9280537"},{"key":"5957_CR36","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1109\/TIFS.2019.2928622","volume":"15","author":"Y Zi","year":"2020","unstructured":"Zi Y, Gao H, Cheng Z, Liu Y (2020) An end-to-end attack on text CAPTCHAs. IEEE Transactions Information Forensics Secur 15:753\u2013766. https:\/\/doi.org\/10.1109\/TIFS.2019.2928622","journal-title":"IEEE Transactions Information Forensics Secur"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05957-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-05957-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05957-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,24]],"date-time":"2021-10-24T07:05:38Z","timestamp":1635059138000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-05957-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,12]]},"references-count":36,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["5957"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-05957-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2021,4,12]]},"assertion":[{"value":"20 August 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}