{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T08:32:03Z","timestamp":1761294723627,"version":"3.37.3"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972143, 61972142"],"award-info":[{"award-number":["61972143, 61972142"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-19122-x","type":"journal-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T07:03:21Z","timestamp":1712905401000},"page":"1303-1315","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhancing image steganography security via universal adversarial perturbations"],"prefix":"10.1007","volume":"84","author":[{"given":"Lan","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dewang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2734-659X","authenticated-orcid":false,"given":"Gaobo","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,12]]},"reference":[{"issue":"3.4","key":"19122_CR1","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1147\/sj.353.0313","volume":"35","author":"W Bender","year":"1996","unstructured":"Bender W, Gruhl D, Morimoto N et al (1996) Techniques for data hiding. IBM Syst J 35(3.4):313\u2013336","journal-title":"IBM Syst J"},{"key":"19122_CR2","first-page":"161","volume-title":"Using high-dimensional image models to perform highly undetectable steganography","author":"T Pevn\u00fd","year":"2010","unstructured":"Pevn\u00fd T, Filler T, Bas P (2010) Using high-dimensional image models to perform highly undetectable steganography. Berlin, Heidelberg, International Workshop on Information Hiding. Springer, pp 161\u2013177"},{"issue":"2","key":"19122_CR3","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1109\/TIFS.2010.2045842","volume":"5","author":"T Pevny","year":"2010","unstructured":"Pevny T, Bas P, Fridrich J (2010) Steganalysis by subtractive pixel adjacency matrix. IEEE Trans Inf Forensics Secur 5(2):215\u2013224","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"19122_CR4","doi-asserted-by":"crossref","unstructured":"Holub V, Fridrich J (2012) Designing steganographic distortion using directional filters. IEEE international workshop on information forensics and security (WIFS). IEEE, pp 234-239","DOI":"10.1109\/WIFS.2012.6412655"},{"key":"19122_CR5","doi-asserted-by":"crossref","unstructured":"Holub V, Fridrich J (2013) Digital image steganography using universal distortion. Proceedings of the first ACM workshop on information hiding and multimedia security, pp 59-68","DOI":"10.1145\/2482513.2482514"},{"key":"19122_CR6","doi-asserted-by":"crossref","unstructured":"Li B, Wang M, Huang J et al (2014) A new cost function for spatial image steganography. IEEE international conference on image processing (ICIP). IEEE, pp 4206-4210","DOI":"10.1109\/ICIP.2014.7025854"},{"issue":"1","key":"19122_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1687-417X-2014-1","volume":"2014","author":"V Holub","year":"2014","unstructured":"Holub V, Fridrich J, Denemark T (2014) Universal distortion function for steganography in an arbitrary domain. EURASIP J Inf Secur 2014(1):1\u201313","journal-title":"EURASIP J Inf Secur"},{"issue":"5","key":"19122_CR8","doi-asserted-by":"publisher","first-page":"814","DOI":"10.1109\/TIFS.2014.2312817","volume":"9","author":"L Guo","year":"2014","unstructured":"Guo L, Ni J, Shi YQ (2014) Uniform embedding for efficient JPEG steganography. IEEE Trans Inf Forensics Secur 9(5):814\u2013825","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"12","key":"19122_CR9","doi-asserted-by":"publisher","first-page":"2669","DOI":"10.1109\/TIFS.2015.2473815","volume":"10","author":"L Guo","year":"2015","unstructured":"Guo L, Ni J, Su W et al (2015) Using statistical image model for JPEG steganography: Uniform embedding revisited. IEEE Trans Inf Forensics Secur 10(12):2669\u20132680","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"19122_CR10","unstructured":"Farid H (2001) Detecting steganographic messages in digital images"},{"key":"19122_CR11","first-page":"1871","volume":"9","author":"RE Fan","year":"2008","unstructured":"Fan RE, Chang KW, Hsieh CJ et al (2008) LIBLINEAR: A library for large linear classification. J Mach Learn Res 9:1871\u20131874","journal-title":"J Mach Learn Res"},{"key":"19122_CR12","doi-asserted-by":"crossref","unstructured":"Kodovsk\u00fd J, Fridrich J (2011) Steganalysis in high dimensions: Fusing classifiers built on random subspaces. Media watermarking, security, and forensics III. SPIE, 7880, pp 204-216","DOI":"10.1117\/12.872279"},{"issue":"2","key":"19122_CR13","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1109\/TIFS.2011.2175919","volume":"7","author":"J Kodovsky","year":"2011","unstructured":"Kodovsky J, Fridrich J, Holub V (2011) Ensemble classifiers for steganalysis of digital media. IEEE Trans Inf Forensics Secur 7(2):432\u2013444","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"5","key":"19122_CR14","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1109\/LSP.2016.2548421","volume":"23","author":"G Xu","year":"2016","unstructured":"Xu G, Wu HZ, Shi YQ (2016) Structural design of convolutional neural networks for steganalysis. IEEE Signal Process Lett 23(5):708\u2013712","journal-title":"IEEE Signal Process Lett"},{"key":"19122_CR15","doi-asserted-by":"crossref","unstructured":"Deng X, Chen B, Luo W et al (2019) Fast and effective global covariance pooling network for image steganalysis. Proceedings of the ACM workshop on information hiding and multimedia security, pp 230-234","DOI":"10.1145\/3335203.3335739"},{"issue":"5","key":"19122_CR16","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1109\/TIFS.2018.2871749","volume":"14","author":"M Boroumand","year":"2018","unstructured":"Boroumand M, Chen M, Fridrich J (2018) Deep residual network for steganalysis of digital images. IEEE Trans Inf Forensics Secur 14(5):1181\u20131193","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"19122_CR17","unstructured":"Szegedy C, Zaremba W, Sutskever I et al (2013) Intriguing properties of neural networks. Preprint arXiv:1312.6199"},{"key":"19122_CR18","doi-asserted-by":"crossref","unstructured":"Nguyen A, Yosinski J, Clune J (2015) Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. Proceedings of the IEEE conference on computer vision and pattern recognition, pp 427-436","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"19122_CR19","doi-asserted-by":"crossref","unstructured":"Zhang Y, Zhang W, Chen K et al (2018) Adversarial examples against deep neural network based steganalysis. Proceedings of the 6th ACM workshop on information hiding and multimedia security, pp 67\u201372","DOI":"10.1145\/3206004.3206012"},{"issue":"8","key":"19122_CR20","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 et al (2019) CNN-based adversarial embedding for image steganography. IEEE Trans Inf Forensics Secur 14(8):2074\u20132087","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"19122_CR21","doi-asserted-by":"publisher","first-page":"812","DOI":"10.1109\/TIFS.2020.3021913","volume":"16","author":"S Bernard","year":"2020","unstructured":"Bernard S, Bas P, Klein J et al (2020) Explicit optimization of min max steganographic game. IEEE Trans Inf Forensics Secur 16:812\u2013823","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"19122_CR22","doi-asserted-by":"crossref","unstructured":"Mo H, Song T, Chen B et al (2019) Enhancing JPEG, steganography using iterative adversarial examples. 2019 IEEE international workshop on information forensics and security (WIFS). IEEE, pp 1\u20136","DOI":"10.1109\/WIFS47025.2019.9035101"},{"key":"19122_CR23","doi-asserted-by":"publisher","first-page":"4621","DOI":"10.1109\/TIFS.2021.3111748","volume":"16","author":"M Liu","year":"2021","unstructured":"Liu M, Luo W, Zheng P et al (2021) A New Adversarial Embedding Method for Enhancing Image Steganography. IEEE Trans Inf Forensics Secur 16:4621\u20134634","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"19122_CR24","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli SM, Fawzi A, Fawzi O et al (2017) Universal adversarial perturbations. Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1765-1773","DOI":"10.1109\/CVPR.2017.17"},{"key":"19122_CR25","unstructured":"Goodfellow I J, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. Preprint arXiv:1412.6572"},{"key":"19122_CR26","unstructured":"Huang S, Papernot N, Goodfellow I et al (2017) Adversarial attacks on neural network policies. Preprint arXiv:1702.02284"},{"key":"19122_CR27","doi-asserted-by":"crossref","unstructured":"Baluja S, Fischer I (2018) Learning to attack: adversarial transformation networks. Thirty-second aaai conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11672"},{"key":"19122_CR28","first-page":"2672","volume":"3","author":"IJ Goodfellow","year":"2014","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M et al (2014) Generative adversarial networks. Adv Neural Inf Process Syst 3:2672\u20132680","journal-title":"Adv Neural Inf Process Syst"},{"key":"19122_CR29","doi-asserted-by":"crossref","unstructured":"Hayes J, Danezis G (2018) Learning universal adversarial perturbations with generative models. 2018 IEEE security and privacy workshops (SPW). IEEE, pp 43\u201349","DOI":"10.1109\/SPW.2018.00015"},{"issue":"3","key":"19122_CR30","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 Forensics Secur 6(3):920\u2013935","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"2","key":"19122_CR31","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TIFS.2015.2486744","volume":"11","author":"V Sedighi","year":"2015","unstructured":"Sedighi V, Cogranne R, Fridrich J (2015) Content-adaptive steganography by minimizing statistical detectability. IEEE Trans Inf Forensics Secur 11(2):221\u2013234","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"19122_CR32","doi-asserted-by":"crossref","unstructured":"Fridrich J, Filler T (2007) Practical methods for minimizing embedding impact in steganography. Security, Steganography, and Watermarking of Multimedia Contents IX. SPIE, 6505, pp 13-27","DOI":"10.1117\/12.697471"},{"key":"19122_CR33","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1109\/TIFS.2019.2922229","volume":"15","author":"J Yang","year":"2019","unstructured":"Yang J, Ruan D, Huang J et al (2019) An embedding cost learning framework using GAN. IEEE Trans Inf Forensics Secur 15:839\u2013851","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"19122_CR34","doi-asserted-by":"crossref","unstructured":"Carlini N, Wagner D (2017) Towards evaluating the robustness of neural networks. IEEE symposium on security and privacy (sp). IEEE, pp 39\u201357","DOI":"10.1109\/SP.2017.49"},{"key":"19122_CR35","doi-asserted-by":"crossref","unstructured":"Chen PY, Zhang H, Sharma Y et al (2017) Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models. Proceedings of the 10th ACM workshop on artificial intelligence and security, pp 15-26","DOI":"10.1145\/3128572.3140448"},{"key":"19122_CR36","doi-asserted-by":"crossref","unstructured":"Bas P, Filler T, Pevn\u1ef3 T (2011) \u201cBreak our steganographic system\u201d: the ins and outs of organizing BOSS. Berlin, Heidelberg, International workshop on information hiding. Springer, pp 59\u201370","DOI":"10.1007\/978-3-642-24178-9_5"},{"key":"19122_CR37","unstructured":"Bas P, Furon T (2007) BOWS-2. [Online]. Available: http:\/\/bows2.ec-lille.fr"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19122-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19122-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19122-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T13:08:58Z","timestamp":1738069738000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19122-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,12]]},"references-count":37,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["19122"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19122-x","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2024,4,12]]},"assertion":[{"value":"21 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2024","order":4,"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":"Conflict of interest"}}]}}