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Generative steganography (GS) is an emerging technique that generates stego images directly from secret data.
Learning multiple layers of features from tiny images
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Rich model for steganalysis of color images. In 2014 IEEE International Workshop on Information Forensics and Security (WIFS) . IEEE, 185–190
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Coverless information hiding based on generative adversarial networks
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Deep learning hierarchical representations for image steganalysis
Jian Ye, Jiangqun Ni, and Yang Yi. 2017 · 2017
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A novel image steganography method via deep convolutional generative adversarial networks
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Glow: Generative flow with invertible 1x1 convolutions
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Towards robust image steganography
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon. 2020 · 2020
Cited alongside, same era.
Audio steganography based on iterative adversarial attacks against convolutional neural networks
Junqi Wu, Bolin Chen, Weiqi Luo, and Yanmei Fang. 2020 · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol. 2021 · 2021
Cited alongside, same era.
A review on text steganography techniques
Mohammed Abdul Majeed, Rossilawati Sulaiman, Zarina Shukur, and Mohammad Kamrul Hasan. 2021 · 2021
Cited alongside, same era.
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Alex Nichol, Prafulla Dhariwal, A. Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. 2021 · 2021
Cited alongside, same era.
Digital image steganography: A literature survey
Pratap Chandra Mandal, Imon Mukherjee, Goutam Paul, and BN Chatterji. 2022 · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022 · 2022
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High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 10684–10695
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al · 2022
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Study and investigation of video steganography over uncompressed and compressed domain: a comprehensive review
Rachna Patel, Kalpesh Lad, and Mukesh Patel. 2021 · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision. In International conference on machine learning . PMLR, 8748–8763
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Image steganography: A review of the recent advances
Nandhini Subramanian, Omar Elharrouss, Somaya Al-Maadeed, and Ahmed Bouridane. 2021 · 2021
Cited alongside, same era.
Image Disentanglement Autoencoder for Steganography Without Embedding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2303–2312
Xiyao Liu, Ziping Ma, Junxing Ma, Jian Zhang, Gerald Schaefer, and Hui Fang. 2022 · 2022
Cited alongside, same era.
Generative Steganography Network. In Proceedings of the 30th ACM International Conference on Multimedia . 1621–1629
Ping Wei, Sheng Li, Xinpeng Zhang, Ge Luo, Zhenxing Qian, and Qing Zhou. 2022a
Cited in the paper.
Ping Wei, Ge Luo, Qi Song, Xinpeng Zhang, Zhenxing Qian, and Sheng Li. 2022b · 2022
Later among the works it cites.
Diffusion models: A comprehensive survey of methods and applications
Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang. 2022 · 2022
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Diffusion models in vision: A survey
Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah. 2023 · 2023
Closest in time.
Image steganalysis using deep learning: a systematic review and open research challenges
Numrena Farooq and Arvind Selwal. 2023 · 2023
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Video steganography: recent advances and challenges
Jayakanth Kunhoth, Nandhini Subramanian, Somaya Al-Maadeed, and Ahmed Bouridane. 2023 · 2023
Closest in time.