GAN-based image steganography by exploiting transform domain knowledge with deep networks
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00530-024-01427-4.pdf
Reference38 articles.
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2. Kadhim, I.J., Premaratne, P., Vial, P.J., Halloran, B.: Comprehensive survey of image steganography: techniques, evaluations, and trends in future research. Neurocomputing 335, 299–326 (2019)
3. Singh, O.P., Singh, A.K., Srivastava, G., Kumar, N.: Image watermarking using soft computing techniques: a comprehensive survey. Multimed. Tools Appl. 80(20), 30367–30398 (2021)
4. Singh, O.P., Singh, K.N., Baranwal, N., Agrawal, A.K., Singh, A.K., Zhou, H.: Hidemarks: hiding multiple marks for robust medical data sharing using IWT-LSB. Multimed. Tools Appl. 83(8), 24919–24937 (2024)
5. Tang, W., Tan, S., Li, B., Huang, J.: Automatic steganographic distortion learning using a generative adversarial network. IEEE Signal Process. Lett. 24(10), 1547–1551 (2017)
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