StegaDDPM: Generative Image Steganography based on Denoising Diffusion Probabilistic Model-Reference-Cited by-同舟云学术

StegaDDPM: Generative Image Steganography based on Denoising Diffusion Probabilistic Model

Published:2023-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Multimedia
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Author:

Peng Yinyin¹^ORCID,Hu Donghui¹^ORCID,Wang Yaofei¹^ORCID,Chen Kejiang²^ORCID,Pei Gang¹^ORCID,Zhang Weiming²^ORCID

Affiliation:

1. Hefei University of Technology, Hefei, China

2. University of Science and Technology of China, Hefei, China

Funder

Anhui Science and Technology Key Special Program

the Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3612514

Reference48 articles.

1. Shumeet Baluja . 2017. Hiding images in plain sight: Deep steganography. Advances in neural information processing systems 30 ( 2017 ). Shumeet Baluja. 2017. Hiding images in plain sight: Deep steganography. Advances in neural information processing systems 30 (2017).

2. Deep Residual Network for Steganalysis of Digital Images

3. An Information-Theoretic Model for Steganography

4. Jooyoung Choi , Sungwon Kim , Yonghyun Jeong , Youngjune Gwon , and Sungroh Yoon . 2021 . Ilvr: Conditioning method for denoising diffusion probabilistic models. arXiv preprint arXiv:2108.02938 (2021). Jooyoung Choi, Sungwon Kim, Yonghyun Jeong, Youngjune Gwon, and Sungroh Yoon. 2021. Ilvr: Conditioning method for denoising diffusion probabilistic models. arXiv preprint arXiv:2108.02938 (2021).

5. StarGAN v2: Diverse Image Synthesis for Multiple Domains

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