{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T06:53:05Z","timestamp":1768805585446,"version":"3.49.0"},"reference-count":33,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T00:00:00Z","timestamp":1639612800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006245","name":"Ministry of Science and Technology","doi-asserted-by":"publisher","award":["MOST 109-2221-E-005 -057 -MY2"],"award-info":[{"award-number":["MOST 109-2221-E-005 -057 -MY2"]}],"id":[{"id":"10.13039\/501100006245","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>With the rapid development of information technology, the transmission of information has become convenient. In order to prevent the leakage of information, information security should be valued. Therefore, the data hiding technique has become a popular solution. The reversible data hiding technique (RDH) in particular uses symmetric encoding and decoding algorithms to embed the data into the cover carrier. Not only can the secret data be transmitted without being detected and retrieved completely, but the cover carrier also can be recovered without distortion. Moreover, the encryption technique can protect the carrier and the hidden data. However, the encrypted carrier is a form of ciphertext, which has a strong probability to attract the attention of potential attackers. Thus, this paper uses the generative adversarial networks (GAN) to generate meaningful encrypted images for RDH. A four-stage network architecture is designed for the experiment, including the hiding network, the encryption\/decryption network, the extractor, and the recovery network. In the hiding network, the secret data are embedded into the cover image through residual learning. In the encryption\/decryption network, the cover image is encrypted into a meaningful image, called the marked image, through GMEI-GAN, and then the marked image is restored to the decrypted image via the same architecture. In the extractor, 100% of the secret data are extracted through the residual learning framework, same as the hiding network. Lastly, in the recovery network, the cover image is reconstructed with the decrypted image and the retrieved secret data through the convolutional neural network. The experimental results show that using the PSNR\/SSIM as the criteria, the stego image reaches 45.09 dB\/0.9936 and the marked image achieves 38.57 dB\/0.9654. The proposed method not only increases the embedding capacity but also maintains high image quality in the stego images and marked images.<\/jats:p>","DOI":"10.3390\/sym13122438","type":"journal-article","created":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T21:32:40Z","timestamp":1639690360000},"page":"2438","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Applying GMEI-GAN to Generate Meaningful Encrypted Images in Reversible Data Hiding Techniques"],"prefix":"10.3390","volume":"13","author":[{"given":"Chwei-Shyong","family":"Tsai","sequence":"first","affiliation":[{"name":"Department of Management Information Systems, National Chung Hsing University, Taichung 402, Taiwan"}]},{"given":"Hsien-Chu","family":"Wu","sequence":"additional","affiliation":[{"name":"Language Center, National Chin-Yi University of Technology, Taichung 411, Taiwan"},{"name":"Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung 404, Taiwan"}]},{"given":"Yu-Wen","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Management Information Systems, National Chung Hsing University, Taichung 402, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7873-4018","authenticated-orcid":false,"given":"Josh Jia-Ching","family":"Ying","sequence":"additional","affiliation":[{"name":"Department of Management Information Systems, National Chung Hsing University, Taichung 402, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,16]]},"reference":[{"key":"ref_1","first-page":"223","article-title":"Digital watermarking and data hiding: Technologies and applications","volume":"3","author":"Zeng","year":"1998","journal-title":"Invited Talk Proc. Int. Conf. Inf. Syst. Anal. Synth."},{"key":"ref_2","unstructured":"Sarkar, T., and Sanyal, S. (2014). Reversible and Irreversible Data Hiding Technique. arXiv."},{"key":"ref_3","unstructured":"Celik, M.U., Sharma, G., Tekalp, A.M., and Saber, E. (2002, January 22\u201325). Reversible data hiding. Proceedings of the International Conference on Image Processing, Rochester, NY, USA."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"986842","DOI":"10.1155\/S1110865702000537","article-title":"Lossless Data Embedding\u2014New Paradigm in Digital Watermarking","volume":"2002","author":"Fridrich","year":"2002","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1109\/TIP.2004.840686","article-title":"Lossless generalized-LSB data embedding","volume":"14","author":"Celik","year":"2005","journal-title":"IEEE Trans. Image Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1109\/TCSVT.2003.815962","article-title":"Reversible data embedding using a difference expansion","volume":"13","author":"Tian","year":"2003","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_7","unstructured":"Thodi, D.M., and Rodriguez, J.J. (2004, January 24\u201327). Prediction-error based reversible watermarking. Proceedings of the 2004 International Conference on Image Processing, 2004. ICIP\u201904, Singapore."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/TCSVT.2006.869964","article-title":"Reversible data hiding","volume":"16","author":"Ni","year":"2006","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1109\/TCSVT.2009.2017409","article-title":"Reversible Data Hiding Based on Histogram Modification of Pixel Differences","volume":"19","author":"Tai","year":"2009","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1109\/LSP.2011.2114651","article-title":"Reversible Data Hiding in Encrypted Image","volume":"18","author":"Zhang","year":"2011","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1109\/TIFS.2011.2176120","article-title":"Separable Reversible Data Hiding in Encrypted Image","volume":"7","author":"Zhang","year":"2012","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gayathri, C., and Karthigaikumar, P. (2014, January 13\u201314). RDH technique for image encryption. Proceedings of the 2014 International Conference on Electronics and Communication Systems (ICECS), Coimbatore, India.","DOI":"10.1109\/ECS.2014.6892824"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1109\/TIFS.2013.2248725","article-title":"Reversible Data Hiding in Encrypted Images by Reserving Room Before Encryption","volume":"8","author":"Ma","year":"2013","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1109\/TMM.2016.2569497","article-title":"Reversible Data Hiding in Encrypted Images by Reversible Image Transformation","volume":"18","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Multimed."},{"key":"ref_15","unstructured":"LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., and Jackel, L. (1990). Handwritten digit recognition with a back-propagation network. Advances in Neural Information Processing Systems: 2, Proceedings of the NIPS\u201989, Denver, CO, USA, 27\u201330 November 1989, Morgan Kaufmann."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_17","unstructured":"Chellapilla, K., Puri, S., and Simard, P. (2006, January 1). High performance convolutional neural networks for document processing. Proceedings of the 10th International Workshop on Frontiers in Handwriting Recognition, La Baule, France."},{"key":"ref_18","first-page":"1097","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_19","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_21","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, K.M., Zhang, X.Y., Ren, S.Q., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_23","first-page":"102808","article-title":"Multi-directional block based PVD and modulus function image steganography to avoid FOBP and IEP","volume":"58","author":"Sahu","year":"2021","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1515\/comp-2020-0136","article-title":"Digital image steganography and steganalysis: A journey of the past three decades","volume":"10","author":"Sahu","year":"2020","journal-title":"Open Comput. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4932782","DOI":"10.1155\/2019\/4932782","article-title":"Generative reversible data hiding by image-to-image translation via GANS","volume":"2019","author":"Zhang","year":"2019","journal-title":"Secur. Commun. Netw."},{"key":"ref_26","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhu, J.-Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_28","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_29","first-page":"2069","article-title":"Hiding images in plain sight: Deep steganography","volume":"30","author":"Baluja","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"9314","DOI":"10.1109\/ACCESS.2019.2891247","article-title":"Reversible Image Steganography Scheme Based on a U-Net Structure","volume":"7","author":"Duan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_31","first-page":"5","article-title":"The pascal visual object classes challenge 2012 (voc2012) development kit","volume":"8","author":"Everingham","year":"2011","journal-title":"Pattern Anal. Stat. Model. Comput. Learn. Tech. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hor\u00e9, A., and Ziou, D. (2010, January 23\u201326). Image Quality Metrics: PSNR vs. SSIM. Proceedings of the 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.579"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/12\/2438\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:50:14Z","timestamp":1760169014000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/12\/2438"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,16]]},"references-count":33,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["sym13122438"],"URL":"https:\/\/doi.org\/10.3390\/sym13122438","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,16]]}}}