{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T11:55:43Z","timestamp":1770292543161,"version":"3.49.0"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61902263"],"award-info":[{"award-number":["61902263"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"crossref","award":["2020M673276"],"award-info":[{"award-number":["2020M673276"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s10489-022-04321-6","type":"journal-article","created":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T05:00:21Z","timestamp":1669870821000},"page":"16059-16076","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Improving security for image steganography using content-adaptive adversarial perturbations"],"prefix":"10.1007","volume":"53","author":[{"given":"Jie","family":"Luo","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3121-0599","authenticated-orcid":false,"given":"Peisong","family":"He","sequence":"additional","affiliation":[]},{"given":"Jiayong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Hongxia","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Chunwang","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Qiang","family":"Xia","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,1]]},"reference":[{"key":"4321_CR1","doi-asserted-by":"publisher","unstructured":"Harmsen JJ, Pearlman WA (2003) Steganalysis of additive-noise modelable information hiding. In: Security and watermarking of multimedia contents V. SPIE, vol 5020. pp 131\u2013142, DOI https:\/\/doi.org\/10.1117\/12.476813https:\/\/doi.org\/10.1117\/12.476813, (to appear in print)","DOI":"10.1117\/12.476813 10.1117\/12.476813"},{"key":"4321_CR2","doi-asserted-by":"publisher","unstructured":"Huang F, Li B, Huang J (2007) Attack lsb matching steganography by counting alteration rate of the number of neighbourhood gray levels. In: IEEE international conference on image processing, vol 1. pp 401\u2013404, DOI https:\/\/doi.org\/10.1109\/icip.2007.4378976, (to appear in print)","DOI":"10.1109\/icip.2007.4378976"},{"issue":"5","key":"4321_CR3","doi-asserted-by":"publisher","first-page":"1242","DOI":"10.1109\/tifs.2017.2780805","volume":"13","author":"B Li","year":"2018","unstructured":"Li B, Li Z, Zhou S, Tan S, Zhang X (2018) New steganalytic features for spatial image steganography based on derivative filters and threshold lbp operator. IEEE Trans Inf Forensic Secur 13(5):1242\u20131257. https:\/\/doi.org\/10.1109\/tifs.2017.2780805","journal-title":"IEEE Trans Inf Forensic Secur"},{"issue":"2","key":"4321_CR4","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1109\/TIFS.2010.2045842","volume":"5","author":"T Pevn\u00fd","year":"2010","unstructured":"Pevn\u00fd T, Bas P, Fridrich J (2010a) Steganalysis by subtractive pixel adjacency matrix. IEEE Trans Inf Forensic Secur 5(2):215\u2013224. https:\/\/doi.org\/10.1109\/Tifs.2010.2045842","journal-title":"IEEE Trans Inf Forensic Secur"},{"issue":"3","key":"4321_CR5","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1109\/tifs.2012.2190402","volume":"7","author":"J Fridrich","year":"2012","unstructured":"Fridrich J, Kodovsk\u00fd J (2012) Rich models for steganalysis of digital images. IEEE Trans Inf Forensic Secur 7(3):868\u2013882. https:\/\/doi.org\/10.1109\/tifs.2012.2190402","journal-title":"IEEE Trans Inf Forensic Secur"},{"key":"4321_CR6","doi-asserted-by":"crossref","unstructured":"Pevn\u00fd T, Filler T, Bas P (2010b) Using high-dimensional image models to perform highly undetectable steganography. In: International workshop on information hiding, pp 161\u2013177","DOI":"10.1007\/978-3-642-16435-4_13"},{"key":"4321_CR7","doi-asserted-by":"crossref","unstructured":"Holub Vojt\u011bch, Fridrich Jessica (2012) Designing steganographic distortion using directional filters. In: IEEE workshop on information forensic and security, pp 234\u2013239","DOI":"10.1109\/WIFS.2012.6412655"},{"issue":"1","key":"4321_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1687-417X-2014-1","volume":"2014","author":"Vojt\u011bch Holub","year":"2014","unstructured":"Holub Vojt\u011bch, Fridrich Jessica, Denemark Tom\u00e1\u0161 (2014) Universal distortion function for steganography in an arbitrary domain. Eurasip J Inf Secur 2014(1):1\u201313","journal-title":"Eurasip J Inf Secur"},{"key":"4321_CR9","doi-asserted-by":"crossref","unstructured":"Li B, Wang M, Huang J, Li X (2014) A new cost function for spatial image steganography. In: IEEE international conference on image processing, pp 4206\u20134210","DOI":"10.1109\/ICIP.2014.7025854"},{"issue":"2","key":"4321_CR10","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TIFS.2015.2486744","volume":"11","author":"V Sedighi","year":"2016","unstructured":"Sedighi V, Cogranne R, Fridrich J (2016) Content-adaptive steganography by minimizing statistical detectability. IEEE Trans Inf Forensic Secur 11(2):221\u2013234. https:\/\/doi.org\/10.1109\/Tifs.2015.2486744https:\/\/doi.org\/10.1109\/Tifs.2015.2486744","journal-title":"IEEE Trans Inf Forensic Secur"},{"issue":"3","key":"4321_CR11","doi-asserted-by":"publisher","first-page":"920","DOI":"10.1109\/TIFS.2011.2134094","volume":"6","author":"T Filler","year":"2011","unstructured":"Filler T, Judas J, Fridrich J (2011) Minimizing additive distortion in steganography using syndrome-trellis codes. IEEE Trans Inf Forensic Secur 6(3):920\u2013935. https:\/\/doi.org\/10.1109\/Tifs.2011.2134094https:\/\/doi.org\/10.1109\/Tifs.2011.2134094","journal-title":"IEEE Trans Inf Forensic Secur"},{"issue":"5","key":"4321_CR12","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1109\/lsp.2016.2548421","volume":"23","author":"G Xu","year":"2016","unstructured":"Xu G, Wu H-Z, Shi Y-Q (2016a) Structural design of convolutional neural networks for steganalysis. IEEE Signal Process Lett 23(5):708\u2013712. https:\/\/doi.org\/10.1109\/lsp.2016.2548421","journal-title":"IEEE Signal Process Lett"},{"issue":"11","key":"4321_CR13","doi-asserted-by":"publisher","first-page":"2545","DOI":"10.1109\/tifs.2017.2710946","volume":"12","author":"J Ye","year":"2017","unstructured":"Ye J, Ni J, Yi Y (2017) Deep learning hierarchical representations for image steganalysis. IEEE Trans Inf Forensic Secur 12(11):2545\u20132557. https:\/\/doi.org\/10.1109\/tifs.2017.2710946","journal-title":"IEEE Trans Inf Forensic Secur"},{"key":"4321_CR14","doi-asserted-by":"crossref","unstructured":"Yedroudj M, Comby F, Chaumont M (2018) Yedroudj-net: an efficient cnn for spatial steganalysis. In: IEEE international conference on acoustics, speech and signal processing, pp 2092\u20132096","DOI":"10.1109\/ICASSP.2018.8461438"},{"issue":"5","key":"4321_CR15","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1109\/tifs.2018.2871749","volume":"14","author":"M Boroumand","year":"2019","unstructured":"Boroumand M, Chen M, Fridrich J (2019) Deep residual network for steganalysis of digital images. IEEE Trans Inf Forensic Secur 14(5):1181\u20131193. https:\/\/doi.org\/10.1109\/tifs.2018.2871749","journal-title":"IEEE Trans Inf Forensic Secur"},{"key":"4321_CR16","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1109\/tifs.2019.2936913","volume":"15","author":"R Zhang","year":"2020","unstructured":"Zhang R, Zhu F, Liu J, Liu G (2020) Depth-wise separable convolutions and multi-level pooling for an efficient spatial cnn-based steganalysis. IEEE Trans Inf Forensic Secur 15:1138\u20131150. https:\/\/doi.org\/10.1109\/tifs.2019.2936913","journal-title":"IEEE Trans Inf Forensic Secur"},{"issue":"10","key":"4321_CR17","doi-asserted-by":"publisher","first-page":"1547","DOI":"10.1109\/LSP.2017.2745572","volume":"24","author":"W Tang","year":"2017","unstructured":"Tang W, Tan S, Li B, Huang J (2017) Automatic steganographic distortion learning using a generative adversarial network. IEEE Signal Process Lett 24(10):1547\u20131551. https:\/\/doi.org\/10.1109\/lsp.2017.2745572","journal-title":"IEEE Signal Process Lett"},{"key":"4321_CR18","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1109\/TIFS.2019.2922229","volume":"15","author":"J Yang","year":"2020","unstructured":"Yang J, Ruan D, Huang J, Kang X, Shi Y-Q (2020) An embedding cost learning framework using gan. IEEE Trans Inf Forensic Secur 15:839\u2013851. https:\/\/doi.org\/10.1109\/tifs.2019.2922229","journal-title":"IEEE Trans Inf Forensic Secur"},{"key":"4321_CR19","doi-asserted-by":"publisher","first-page":"952","DOI":"10.1109\/tifs.2020.3025438","volume":"16","author":"W Tang","year":"2021","unstructured":"Tang W, Li B, Barni M, Li J, Huang J (2021) An automatic cost learning framework for image steganography using deep reinforcement learning. IEEE Trans Inf Forensic Secur 16:952\u2013967. https:\/\/doi.org\/10.1109\/tifs.2020.3025438","journal-title":"IEEE Trans Inf Forensic Secur"},{"issue":"9","key":"4321_CR20","doi-asserted-by":"publisher","first-page":"2805","DOI":"10.1109\/TNNLS.2018.2886017","volume":"30","author":"X Yuan","year":"2019","unstructured":"Yuan X, He P, Zhu Q, Li X (2019) Adversarial examples: attacks and defenses for deep learning. IEEE Trans Neural Netw Learn Syst 30(9):2805\u20132824. https:\/\/doi.org\/10.1109\/TNNLS.2018.2886017https:\/\/doi.org\/10.1109\/TNNLS.2018.2886017. URL https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30640631","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4321_CR21","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2015) Explaining and harnessing adversaral examples. In: Learning representations"},{"key":"4321_CR22","doi-asserted-by":"crossref","unstructured":"Zhang Y, Zhang W, Chen K, Liu J, Liu Y, Yu N (2018) Adversarial examples against deep neural network based steganalysis. In: Information hiding and multimedia security, pp 67\u201372, DOI , (to appear in print)","DOI":"10.1145\/3206004.3206012"},{"key":"4321_CR23","doi-asserted-by":"publisher","first-page":"103325","DOI":"10.1016\/j.jvcir.2021.103325","volume":"80","author":"C Qin","year":"2021","unstructured":"Qin C, Zhang W, Dong X, Zha H, Yu N (2021a) Adversarial steganography based on sparse cover enhancement. J Vis Commun Image Represent 80:103325. https:\/\/doi.org\/10.1016\/j.jvcir.2021.103325https:\/\/doi.org\/10.1016\/j.jvcir.2021.103325","journal-title":"J Vis Commun Image Represent"},{"key":"4321_CR24","doi-asserted-by":"crossref","unstructured":"Volkhonskiy Denis, Nazarov Ivan, Burnaev Evgeny (2020) Steganographic generative adversarial networks. In: Twelfth international conference on machine vision, vol 11433. pp 114333m","DOI":"10.1117\/12.2559429"},{"key":"4321_CR25","doi-asserted-by":"publisher","unstructured":"Shi H, Dong J, Wang W, Qian Y, Zhang X (2017) Ssgan: secure steganography based on generative adversarial networks. In: Pacific rim conference on multimedia, Springer, pp 534\u2013544. https:\/\/doi.org\/10.1007\/978-3-319-77380-3_51","DOI":"10.1007\/978-3-319-77380-3_51"},{"key":"4321_CR26","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1007\/978-3-030-01267-0_40","volume":"11219","author":"J Zhu","year":"2018","unstructured":"Zhu J, Kaplan R, Johnson J, Li F-F (2018) Hidden: hiding data with deep networks. Comput Vis 11219:682\u2013697. https:\/\/doi.org\/10.1007\/978-3-030-01267-0_40","journal-title":"Comput Vis"},{"key":"4321_CR27","unstructured":"Hayes J, Danezis G (2017) Generating steganographic images via adversarial training, Adv Neural Inf Process Syst :30"},{"issue":"8","key":"4321_CR28","doi-asserted-by":"publisher","first-page":"9441","DOI":"10.1007\/s10489-021-02938-7","volume":"52","author":"H Kheddar","year":"2022","unstructured":"Kheddar H, Meg\u00edas D (2022) High capacity speech steganography for the G723.1 coder based on quantised line spectral pairs interpolation and CNN auto-encoding. Appl Intell 52(8):9441\u20139459","journal-title":"Appl Intell"},{"key":"4321_CR29","doi-asserted-by":"publisher","unstructured":"Zhang W, Zha H, Qin C, Yu N (2019) Direct adversarial attack on stego sandwiched between black boxes. In: 2019 IEEE international conference on image processing (ICIP), pp 2284-2288, DOI https:\/\/doi.org\/10.1109\/ICIP.2019.8804415, (to appear in print)","DOI":"10.1109\/ICIP.2019.8804415"},{"issue":"8","key":"4321_CR30","doi-asserted-by":"publisher","first-page":"2074","DOI":"10.1109\/TIFS.2019.2891237","volume":"14","author":"W Tang","year":"2019","unstructured":"Tang W, Li B, Tan S, Barni M, Huang J (2019) Cnn-based adversarial embedding for image steganography. IEEE Trans Inf Forensic Secur 14(8):2074\u20132087. https:\/\/doi.org\/10.1109\/tifs.2019.2891237","journal-title":"IEEE Trans Inf Forensic Secur"},{"key":"4321_CR31","doi-asserted-by":"crossref","unstructured":"Bernard S, Pevn\u00fd T, Bas P, Klein J (2019) Exploiting adversarial embeddings for better steganography","DOI":"10.1145\/3335203.3335737"},{"key":"4321_CR32","doi-asserted-by":"crossref","unstructured":"Qin X, Tan S, Tang W, Li B, Huang J (2021b) Image steganography based on iterative adversarial perturbations onto a synchronized-directions sub-image","DOI":"10.1109\/ICASSP39728.2021.9414055"},{"key":"4321_CR33","doi-asserted-by":"publisher","first-page":"4621","DOI":"10.1109\/TIFS.2021.3111748","volume":"16","author":"M Liu","year":"2021","unstructured":"Liu M, Luo WP, Zheng P, Huang J (2021) A new adversarial embedding method for enhancing image steganography. IEEE Trans Inf Forensic Secur 16:4621\u20134634. https:\/\/doi.org\/10.1109\/Tifs.2021.3111748https:\/\/doi.org\/10.1109\/Tifs.2021.3111748","journal-title":"IEEE Trans Inf Forensic Secur"},{"key":"4321_CR34","doi-asserted-by":"crossref","unstructured":"Liu M, Song T, Luo W, Zheng P, Huang J (2022) Adversarial steganography embedding via stego generation and selection. IEEE Transactions on Dependable and Secure Computing","DOI":"10.1109\/TDSC.2022.3182041"},{"key":"4321_CR35","doi-asserted-by":"crossref","unstructured":"Mo H, Song T, Chen B, Luo W, Huang J (2019) Enhancing jpeg steganography using iterative adversarial examples. IEEE International Workshop on Information Forensics and Security","DOI":"10.1109\/WIFS47025.2019.9035101"},{"issue":"8","key":"4321_CR36","doi-asserted-by":"publisher","first-page":"5110","DOI":"10.1109\/tcsvt.2022.3148406","volume":"32","author":"X Qin","year":"2022","unstructured":"Qin X, Li B, Tan S, Tang W, Huang J (2022) Gradually enhanced adversarial perturbations on color pixel vectors for image steganography. IEEE Trans Circ Syst Video Technol 32(8):5110\u20135123. https:\/\/doi.org\/10.1109\/tcsvt.2022.3148406","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"4321_CR37","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1109\/LSP.2019.2963180","volume":"27","author":"L Zhou","year":"2020","unstructured":"Zhou L, Feng G, Shen L, Zhang X (2020) On security enhancement of steganography via generative adversarial image. IEEE Signal Process Lett 27:166\u2013170. https:\/\/doi.org\/10.1109\/lsp.2019.2963180","journal-title":"IEEE Signal Process Lett"},{"issue":"4","key":"4321_CR38","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1109\/TIFS.2015.2507159","volume":"11","author":"W Tang","year":"2015","unstructured":"Tang W, Li H, Luo W, Huang J (2015) Adaptive steganalysis based on embedding probabilities of pixels. IEEE Trans Inf Forensic Secur 11(4):734\u2013745. https:\/\/doi.org\/10.1109\/tifs.2015.2507159","journal-title":"IEEE Trans Inf Forensic Secur"},{"key":"4321_CR39","doi-asserted-by":"publisher","unstructured":"Denemark T, Sedighi V, Holub V, Cogranne R, Fridrich J (2014) Selection-channel-aware rich model for steganalysis of digital images. In: IEEE international workshop on information forensics and security, pp 48\u201353, DOI https:\/\/doi.org\/10.1109\/wifs.2014.7084302https:\/\/doi.org\/10.1109\/wifs.2014.7084302, (to appear in print)","DOI":"10.1109\/wifs.2014.7084302 10.1109\/wifs.2014.7084302"},{"key":"4321_CR40","doi-asserted-by":"publisher","unstructured":"Xu G, Wu H-Z, Shi YQ (2016b) Ensemble of cnns for steganalysis: An empirical study. In: Information hiding and multimedia security, pp 103\u2013107, DOI https:\/\/doi.org\/10.1145\/2909827.2930798, (to appear in print)","DOI":"10.1145\/2909827.2930798"},{"issue":"3","key":"4321_CR41","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.1109\/tdsc.2019.2929047","volume":"18","author":"Z Wang","year":"2019","unstructured":"Wang Z, Song M, Zheng S, Zhang Z, Song Y, Wang Q (2019) Invisible adversarial attack against deep neural networks: an adaptive penalization approach. IEEE Trans Dependable Secure Comput 18 (3):1474\u20131488. https:\/\/doi.org\/10.1109\/tdsc.2019.2929047","journal-title":"IEEE Trans Dependable Secure Comput"},{"issue":"1","key":"4321_CR42","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/0031-3203(95)00067-4","volume":"29","author":"T Ojala","year":"1996","unstructured":"Ojala T, Pietik\u00e4inen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29(1):51\u201359","journal-title":"Pattern Recogn"},{"issue":"7","key":"4321_CR43","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","volume":"24","author":"T Ojala","year":"2002","unstructured":"Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971\u2013 987","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"12","key":"4321_CR44","doi-asserted-by":"publisher","first-page":"4675","DOI":"10.1109\/tim.2019.2900961","volume":"68","author":"B Su","year":"2019","unstructured":"Su B, Chen H, Zhu Y, Liu W, Liu K (2019) Classification of manufacturing defects in multicrystalline solar cells with novel feature descriptor. IEEE Trans Instrum Meas 68(12):4675\u20134688. https:\/\/doi.org\/10.1109\/tim.2019.2900961","journal-title":"IEEE Trans Instrum Meas"},{"issue":"1","key":"4321_CR45","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1109\/tcsvt.2020.2974884","volume":"31","author":"N Zhong","year":"2021","unstructured":"Zhong N, Qian Z, Wang Z, Zhang X, Li X (2021) Batch steganography via generative network. IEEE Trans Circuits Syst Video Technol 31(1):88\u201397. https:\/\/doi.org\/10.1109\/tcsvt.2020.2974884","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"4321_CR46","doi-asserted-by":"publisher","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","volume":"34","author":"R Achanta","year":"2003","unstructured":"Achanta R, Shaji A, Smith K, Lucchi A, Fua P, S\u00fcsstrunk S (2003) 11. IEEE Trans Pattern Anal Mach Intell 34:2274\u20132282","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"4321_CR47","doi-asserted-by":"crossref","unstructured":"Bas P, Filler T, Pevn\u00fd T (2011) Break our steganographic system: the ins and outs of organizing boss. In: International workshop on information hiding, Springer, pp 59\u201370","DOI":"10.1007\/978-3-642-24178-9_5"},{"key":"4321_CR48","unstructured":"Bas P, Furon T (2007) Bows-2. http:\/\/bows2.ec-lille.fr"},{"key":"4321_CR49","doi-asserted-by":"crossref","unstructured":"Weibin Wu, Yuxin Su, Lyu MR, King I (2021) Improving the transferability of adversarial samples with adversarial transformations. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9024\u20139033","DOI":"10.1109\/CVPR46437.2021.00891"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04321-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-04321-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04321-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T04:08:23Z","timestamp":1685592503000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-04321-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,1]]},"references-count":49,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["4321"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-04321-6","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,1]]},"assertion":[{"value":"3 November 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 December 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}