The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Steganographic generative adversarial networks
Open accessAbstract
Steganography is collection of methods to hide secret information (“payload”) within non-secret information “container”). Its counterpart, Steganalysis, is the practice of determining if a message contains a hidden payload, and recovering it if possible. Presence of hidden payloads is typically detected by a binary classifier. In the present study, we propose a new model for generating image-like containers based on Deep Convolutional Generative Adversarial Networks (DCGAN). This approach allows to generate more setganalysis-secure message embedding using standard steganography algorithms. Experiment results demonstrate that the new model successfully deceives the steganography analyzer, and for this reason, can be used in steganographic applications.
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
Conditional Generative Adversarial Nets
Mirza, M., & Osindero, S. (2014) · arXiv · doi:10.48550/arxiv.1411.1784 · Cited by 8,863
Rich Models for Steganalysis of Digital Images
Fridrich, J., & Kodovsky, J. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2190402 · Cited by 2,144
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Nowozin, S., Cseke, B., & Tomioka, R. (2016) · arXiv · doi:10.48550/arxiv.1606.00709 · Cited by 1,183
Cited by
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3130191 · Cited by 1,231
Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions
Saxena, D., & Cao, J. (2022) · ACM Computing Surveys · doi:10.1145/3446374 · Cited by 689
Bandi, A., Adapa, P. V. S. R., & Kuchi, Y. E. V. P. K. (2023) · Future Internet · doi:10.3390/fi15080260 · Cited by 638
How Generative Adversarial Networks and Their Variants Work: An Overview
Hong, Y., Hwang, U., Yoo, J., & Yoon, S. (2020) · ACM Computing Surveys · doi:10.1145/3301282 · Cited by 373
Baluja, S. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2901877 · Cited by 325
Metadata
- Authors
- Volkhonskiy, D., Nazarov, I., & Burnaev, E.
- Venue
- Twelfth International Conference on Machine Vision (ICMV 2019)
- Publisher
- SPIE
- Published
- 2020-01-31
- Pages
- 97
- OpenAlex
- W2605121773
- Cited by
- 224
- References
- 6
- FWCI
- 10.30
- Type
- conference paper
- Language
- en
- Open access
- green
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