{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T12:53:46Z","timestamp":1780923226264,"version":"3.54.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T00:00:00Z","timestamp":1733529600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T00:00:00Z","timestamp":1733529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23B2002"],"award-info":[{"award-number":["U23B2002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62272007"],"award-info":[{"award-number":["No.62272007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cybersecurity"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>With the development of deep learning technology, great progress has been made in the field of coverless steganography based on deep learning technology, including some selection-based steganography methods that use deep learning technology and all generation-based steganography methods, however both of which have their limitations. The former is difficult to meet actual communication requirements in terms of communication capacity and completeness due to the limit of the algorithm. Due to the irreversibility of the process of generating secret images from message codeword, the recovery accuracy of the latter is very poor. To this end, this paper designs a robust joint coverless image steganography scheme called Joint Coverless Image Steganography (JoCS). Firstly, this paper proposes the Semantic Factorization Fitting module (SeFF) and the Transform Domain Steganography module (TrDS). The former adds the secret message to the input vector of the low resolution layer in the StyleGAN generator network, which establishs a mapping rule between message codeword and the coarse feature of the generated image, and then the extractor is used to fit the above mapping rule, which has excellent robustness and completeness; the latter encodes the main content area of the image based on the encoder in VQGAN, and then adds secret message to the latent vector of the encoded image, which achieves the steganography in the latent domain of the image. Secondly, we demonstrate the independence between two modules and the advantages of connecting two modules. By using the image generated in the SeFF module as the cover image in the TrDS module, secondary steganography of a single image is achieved, based on which we design the JoCS scheme. The results show that our scheme breaks through the communication capacity limit in the selection-based coverless methods while guaranteeing 100% completeness, excellent image quality and outstanding robustness against various image attacks. Moreover, our scheme exhibits strong security against detection by multiple steganalysis tools and excellent practicality in practical communication. Finally, this paper also discusses the following three points as further elaboration of the scheme: (1) the advantages of the mapping rule in the SeFF module (2) the verification of the independence between the two modules (3) the flexibility of the joint steganography scheme.<\/jats:p>","DOI":"10.1186\/s42400-024-00299-5","type":"journal-article","created":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T02:01:46Z","timestamp":1733536906000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Robust joint coverless image steganography scheme based on two independent modules"],"prefix":"10.1186","volume":"7","author":[{"given":"Chang","family":"Ren","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8103-0468","authenticated-orcid":false,"given":"Bin","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,7]]},"reference":[{"issue":"5","key":"299_CR2","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 Sec 14(5):1181\u20131193","journal-title":"IEEE Trans Inf Forensics Sec"},{"key":"299_CR3","doi-asserted-by":"crossref","unstructured":"Bui T, Agarwal S, Yu N, Collomosse J (2023) Rosteals: Robust steganography using autoencoder latent space 933\u2013942","DOI":"10.1109\/CVPRW59228.2023.00100"},{"key":"299_CR4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13640-020-0490-z","volume":"2020","author":"Y Cao","year":"2020","unstructured":"Cao Y, Zhou Z, Wu QJ, Yuan C, Sun X (2020) Coverless information hiding based on the generation of anime characters. EURASIP J Image Vid Process 2020:1\u201315","journal-title":"EURASIP J Image Vid Process"},{"key":"299_CR5","doi-asserted-by":"crossref","unstructured":"Esser P, Rombach R, Ommer B (2021) Taming transformers for high-resolution image synthesis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12873\u201312883","DOI":"10.1109\/CVPR46437.2021.01268"},{"issue":"3","key":"299_CR6","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1109\/TIFS.2012.2190402","volume":"7","author":"J Fridrich","year":"2012","unstructured":"Fridrich J, Kodovsky J (2012) Rich models for steganalysis of digital images. IEEE Trans Inf Forensics Sec 7(3):868\u2013882","journal-title":"IEEE Trans Inf Forensics Sec"},{"key":"299_CR7","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"issue":"11","key":"299_CR8","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139\u2013144","journal-title":"Commun ACM"},{"key":"299_CR9","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572"},{"key":"299_CR10","unstructured":"Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30"},{"key":"299_CR11","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho J, Jain A, Abbeel P (2020) Denoising diffusion probabilistic models. Adv Neural Inf Syst 33:6840\u20136851","journal-title":"Adv Neural Inf Syst"},{"key":"299_CR12","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 Sec 2014:1\u201313","journal-title":"EURASIP J Inf Sec"},{"key":"299_CR13","doi-asserted-by":"publisher","first-page":"38303","DOI":"10.1109\/ACCESS.2018.2852771","volume":"6","author":"D Hu","year":"2018","unstructured":"Hu D, Wang L, Jiang W, Zheng S, Li B (2018) A novel image steganography method via deep convolutional generative adversarial networks. IEEE access 6:38303\u201338314","journal-title":"IEEE access"},{"key":"299_CR14","doi-asserted-by":"crossref","unstructured":"Jayasumana S, Ramalingam S, Veit A, Glasner D, Chakrabarti A, Kumar S (2023) Rethinking fid: Towards a better evaluation metric for image generation. arXiv preprint arXiv:2401.09603","DOI":"10.1109\/CVPR52733.2024.00889"},{"key":"299_CR15","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aittala M, Hellsten J, Lehtinen J, Aila T (2020) Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8110\u20138119","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"299_CR16","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aila T (2019) A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401\u20134410","DOI":"10.1109\/CVPR.2019.00453"},{"key":"299_CR17","unstructured":"Kim D, Shin C, Choi J, Jung D, Yoon S (2023) Diffusion-stego: Training-free diffusion generative steganography via message projection. arXiv preprint arXiv:2305.18726"},{"issue":"3","key":"299_CR18","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1109\/TCSVT.2019.2896270","volume":"30","author":"X Liao","year":"2019","unstructured":"Liao X, Yu Y, Li B, Li Z, Qin Z (2019) A new payload partition strategy in color image steganography. IEEE Trans Circ Syst Video Technol 30(3):685\u2013696","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"299_CR19","doi-asserted-by":"crossref","unstructured":"Liu X, Ma Z, Ma J, Zhang J, Schaefer G, Fang H (2022) Image disentanglement autoencoder for steganography without embedding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2303\u20132312","DOI":"10.1109\/CVPR52688.2022.00234"},{"issue":"7","key":"299_CR20","doi-asserted-by":"publisher","first-page":"2779","DOI":"10.1109\/TCSVT.2020.3033945","volume":"31","author":"Y Luo","year":"2020","unstructured":"Luo Y, Qin J, Xiang X, Tan Y (2020) Coverless image steganography based on multi-object recognition. IEEE Trans Circ Syst Video Technol 31(7):2779\u20132791","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"299_CR21","unstructured":"Madry A, Makelov A, Schmidt L, Tsipras D, Vladu A (2017) Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083"},{"issue":"5","key":"299_CR22","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1109\/LSP.2006.870357","volume":"13","author":"J Mielikainen","year":"2006","unstructured":"Mielikainen J (2006) LSB matching revisited. IEEE Sig Process Lett 13(5):285\u2013287","journal-title":"IEEE Sig Process Lett"},{"issue":"9","key":"299_CR23","doi-asserted-by":"publisher","first-page":"5817","DOI":"10.1109\/TCSVT.2022.3161419","volume":"32","author":"F Peng","year":"2022","unstructured":"Peng F, Chen G, Long M (2022) A robust coverless steganography based on generative adversarial networks and gradient descent approximation. IEEE Trans Circ Syst Video Technol 32(9):5817\u20135829","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"299_CR24","doi-asserted-by":"crossref","unstructured":"Peng Y, Hu D, Wang Y, Chen K, Pei G, Zhang W (2023) Stegaddpm: Generative image steganography based on denoising diffusion probabilistic model. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 7143\u20137151","DOI":"10.1145\/3581783.3612514"},{"key":"299_CR25","doi-asserted-by":"crossref","unstructured":"Pevn\u1ef3 T, Bas P, Fridrich J (2009) Steganalysis by subtractive pixel adjacency matrix. In: Proceedings of the 11th ACM Workshop on Multimedia and Security, pp. 75\u201384","DOI":"10.1145\/1597817.1597831"},{"key":"299_CR26","doi-asserted-by":"crossref","unstructured":"Pevn\u1ef3 T, Filler T, Bas P (2010) Using high-dimensional image models to perform highly undetectable steganography. In: Information Hiding: 12th International Conference, IH 2010, Calgary, AB, Canada, June 28-30, 2010, Revised Selected Papers 12, pp. 161\u2013177. Springer","DOI":"10.1007\/978-3-642-16435-4_13"},{"key":"299_CR27","doi-asserted-by":"crossref","unstructured":"Pevn\u1ef3 T, Bas P, Fridrich J (2009) Steganalysis by subtractive pixel adjacency matrix. In: Proceedings of the 11th ACM Workshop on Multimedia and Security, pp. 75\u201384","DOI":"10.1145\/1597817.1597831"},{"key":"299_CR28","unstructured":"RenChang (2024) Supplementary experimental data. Accessed: 2024-06-12. https:\/\/github.com\/rchotcocoa\/JoCS"},{"key":"299_CR29","doi-asserted-by":"crossref","unstructured":"Rombach R, Blattmann A, Lorenz D, Esser P, Ommer B (2022) High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695","DOI":"10.1109\/CVPR52688.2022.01042"},{"issue":"3","key":"299_CR30","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1109\/TCSVT.2020.3001122","volume":"31","author":"W Su","year":"2020","unstructured":"Su W, Ni J, Hu X, Fridrich J (2020) Image steganography with symmetric embedding using Gaussian Markov random field model. IEEE Trans Circ Syst Video Technol 31(3):1001\u20131015","journal-title":"IEEE Trans Circ Syst Video Technol"},{"issue":"2","key":"299_CR31","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1109\/TCSVT.2018.2881118","volume":"29","author":"J Tao","year":"2018","unstructured":"Tao J, Li S, Zhang X, Wang Z (2018) Towards robust image steganography. IEEE Trans Circ Syst Video Technol 29(2):594\u2013600","journal-title":"IEEE Trans Circ Syst Video Technol"},{"issue":"3","key":"299_CR33","first-page":"48","volume":"5","author":"Y Wang","year":"2020","unstructured":"Wang Y, Wu B (2020) An intelligent search method of mapping relation for coverless information hiding. J Cyber Sec 5(3):48\u201361","journal-title":"J Cyber Sec"},{"key":"299_CR32","unstructured":"Wei P, Zhou Q, Wang Z, Qian Z, Zhang X, Li S (2023) Generative steganography diffusion. arXiv preprint arXiv:2305.03472"},{"key":"299_CR44","doi-asserted-by":"publisher","DOI":"10.19363\/J.cnki.cn10-1380\/tn.2024.04.05","author":"B Wu","year":"2024","unstructured":"Wu B, Xue R (2024) A coverless image steganography method using deep learning with feature distribution optimization. J Cyber Security. https:\/\/doi.org\/10.19363\/J.cnki.cn10-1380\/tn.2024.04.05","journal-title":"J Cyber Security"},{"key":"299_CR34","unstructured":"Wu H, Zhang Z, Zhang W, Chen C, Liao L, Li C, Gao Y, Wang A, Zhang E, Sun W, et al (2023) Q-align: Teaching lmms for visual scoring via discrete text-defined levels. arXiv preprint arXiv:2312.17090"},{"issue":"5","key":"299_CR35","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 (2016) Structural design of convolutional neural networks for steganalysis. IEEE Sig Process Lett 23(5):708\u2013712","journal-title":"IEEE Sig Process Lett"},{"key":"299_CR36","doi-asserted-by":"crossref","unstructured":"Xue R, Wang Y (2021) Message drives image: A coverless image steganography framework using multi-domain image translation, 1\u20139. IEEE","DOI":"10.1109\/IJCNN52387.2021.9534043"},{"issue":"11","key":"299_CR37","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 Forensics Sec 12(11):2545\u20132557","journal-title":"IEEE Trans Inf Forensics Sec"},{"key":"299_CR38","doi-asserted-by":"crossref","unstructured":"Yedroudj M, Comby F, Chaumont M (2018) Yedroudj-net: An efficient cnn for spatial steganalysis, 2092\u20132096. IEEE","DOI":"10.1109\/ICASSP.2018.8461438"},{"key":"299_CR39","doi-asserted-by":"crossref","unstructured":"You Z, Ying Q, Li S, Qian Z, Zhang X (2022) Image generation network for covert transmission in online social network. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 2834\u20132842","DOI":"10.1145\/3503161.3548139"},{"key":"299_CR40","unstructured":"Yu J, Zhang X, Xu Y, Zhang J (2023) Cross: Diffusion model makes controllable, robust and secure image steganography. arXiv preprint arXiv:2305.16936"},{"issue":"2","key":"299_CR41","first-page":"435","volume":"18","author":"C Yuan","year":"2017","unstructured":"Yuan C, Xia Z, Sun X (2017) Coverless image steganography based on sift and BOF. J Int Technol 18(2):435\u2013442","journal-title":"J Int Technol"},{"key":"299_CR42","doi-asserted-by":"crossref","unstructured":"Zheng S, Wang L, Ling B, Hu D (2017) Coverless information hiding based on robust image hashing. In: Intelligent Computing Methodologies: 13th International Conference, ICIC 2017, Liverpool, UK, August 7-10, 2017, Proceedings, Part III 13, pp. 536\u2013547. Springer","DOI":"10.1007\/978-3-319-63315-2_47"},{"key":"299_CR43","doi-asserted-by":"crossref","unstructured":"Zhou Z, Sun H, Harit R, Chen X, Sun X (2015) Coverless image steganography without embedding. In: Cloud Computing and Security: First International Conference, ICCCS 2015, Nanjing, China, August 13-15, 2015. Revised Selected Papers 1, pp. 123\u2013132. Springer","DOI":"10.1007\/978-3-319-27051-7_11"}],"container-title":["Cybersecurity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42400-024-00299-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s42400-024-00299-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42400-024-00299-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T03:02:45Z","timestamp":1733540565000},"score":1,"resource":{"primary":{"URL":"https:\/\/cybersecurity.springeropen.com\/articles\/10.1186\/s42400-024-00299-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,7]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["299"],"URL":"https:\/\/doi.org\/10.1186\/s42400-024-00299-5","relation":{},"ISSN":["2523-3246"],"issn-type":[{"value":"2523-3246","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,7]]},"assertion":[{"value":"2 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Both authors declare that they have no Conflict of interest","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}],"article-number":"73"}}