Abstract
This manuscript proposes an end-to-end trainable model, VAE-GAN, engineered to hide messages (image) inside a container (image). The model consists of an embedder network (to hide a message inside the container) and an extractor network(to extract the hidden message from the encoded image). In the proposed method, we employ the generative power of a variational autoencoder with adversarial training to embed images. At the extractor, a vanilla convolutional network with adversarial training has provided the best results with clean extracted images. To analyse the noise sensitivity of the model, the encoded image is subjected to multiple attacks, and it is established that the proposed method is inherently robust towards attacks like Gaussian blurring, rotation, noise, and cropping. However, the model can be trained on any possible attacks to reduce noise sensitivity further. In this manuscript, we explore the application of hiding images inside images, but the method can be extended to hide various combinations of data hiding.
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References
Brock, A., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 (2018)
Radford, A., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: International Conference on Learning Representations (2016)
Agustsson, E., Timofte, R.: Ntire 2017 challenge on single image super-resolution: dataset and study. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1122–1131 (2017)
Baluja, S.: Hiding images in plain sight: Deep steganography. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 2069–2079. Curran Associates, Inc. (2017)
Chang, C.C.: Adversarial learning for invertible steganography. IEEE Access 8, 198425–198435 (2020)
Chen, B.J., Coatrieux, G.: Full 4-d quaternion discrete Fourier transform based watermarking for color images. Digital Signal Process 28(1), 106–119 (2014)
Das, C., Panigrahi, S.: A novel blind robust image watermarking in DCT domain using inter-block coefficient correlation. Int. J. Electron. Commun. 68(3), 244–253 (2014)
Feng, L.P., Zheng, L.B.: A DWT-DCT based blind watermarking algorithm for copyright protection. In: Proceedings of IEEE ICCIST. 7, 455–458 (2010). https://doi.org/10.1109/ICCSIT.2010.5565101
Gandikota, R., Mishra, D.: Hiding audio in images: a deep learning approach. In: Deka, B., Maji, P., Mitra, S., Bhattacharyya, D.K., Bora, P.K., Pal, S.K. (eds.) Pattern Recognition and Machine Intelligence, pp. 389–399. Springer International Publishing, Cham (2019). https://doi.org/10.1007/978-3-030-34872-4_43
GitRepo: Hd-vae-gan (2019). https://github.com/RohitGandikota/Hiding-Images-using-VAE-Genarative-Adversarial-Networks. Accessed 11 Sep 2019
Goodfellow, I.J., Pouget-Abadie, J., Bengio, Y.: Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27, 2672–2680 (2013)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: CVPR (2017)
Ouyang, J., Coatrieux, G., Chen, B., Shu, H.: Color image watermarking based on quaternion Fourier transform and improved uniform log-polar mapping. Comput. Elect. Eng. 46, 419–432 (2015)
Zhu, J., Kaplan, R., Jhonson, J., Li, F.-F.: Hidden: hiding data with deep networks. In: The European Conference on Computer Vision (ECCV), pp. 657–672. Springer, Cham (2018). https://www.springerprofessional.de/hidden-hiding-data-with-deep-networks/16180210
Kandi, H., Mishra, D., Gorthi, S.: Exploring the learning capabilities of convolutional neural networks for robust image watermarking. Comput. Secur. 65, 2506–2510 (2017). https://doi.org/10.1016/j.cose.2016.11.016
Zhang, K.A., Cuesta-Infante, A., Xu, L.: SteganoGAN: High capacity image steganography with GANs. arXiv preprint arXiv:1901.03892, January 2019
Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. In: 2nd International Conference on Learning Representations (ICLR) (2013)
Lee, J.E., Seo, Y.H., Kim, D.W.: Convolutional neural network-based digital image watermarking adaptive to the resolution of image and watermark. Appl. Sci. 10(19), 6854 (2020)
Li, C., Jiang, Y., Cheslyar, M.: Embedding image through generated intermediate medium using deep convolutional generative adversarial network. Comput. Mater. Continua. 56(2), 313–324 (2018)
Liu, Y., Guo, M., Zhang, J., Zhu, Y., Xie, X.: A novel two-stage separable deep learning framework for practical blind watermarking. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 1509–1517 (2019)
Lu, Y., Tai, Y.W., Tang, C.K.: Attribute-guided face generation using conditional cycleGAN. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 282–297. Springer, Cham (2018). https://www.springerprofessional.de/en/attribute-guided-face-generation-using-conditional-cyclegan/16177286
Mun, S.M., Nam, S.M., Jang, H.U., Kim, D., Lee, H.K.: A robust blind watermarking using convolutional neural network. arXiv preprint arXiv:1704.03248 (2017)
Sharma, K., Aggarwal, A., Singhania, T., Gupta, D., Khanna, A.: Hiding data in images using cryptography and deep neural network. arXiv preprint arXiv:1912.10413 (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations, pp. 1–14 (2015)
Sun, Q.T., Niu, Y.G., Wang, Q., Sheng, G.: A blind color image watermarking based on dc component in the spatial domain. Optik. 124(23), 6255–6260 (2013). https://doi.org/10.1016/j.ijleo.2013.05.013
Wu, J., Shi, H., Zhang, S., Lei, Z., Yang, Y., Li, S.Z.: De-mark GAN: removing dense watermark with generative adversarial network. In: International Conference on Biometrics (ICB), pp. 69–74, February 2018. https://doi.org/10.1109/ICB2018.2018.00021
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Gandikota, R., Mishra, D. (2023). HD-VAE-GAN: Hiding Data with Variational Autoencoder Generative Adversarial Networks. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1776. Springer, Cham. https://doi.org/10.1007/978-3-031-31407-0_3
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