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We revisit watermarking techniques based on pre-trained deep networks, in the light of self-supervised approaches.
“Image watermarking using dct domain constraints,”
Adrian G Bors and Ioannis Pitas, · 1996
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“Robust image watermarking in the spatial domain,”
Nikos Nikolaidis and Ioannis Pitas, · 1998
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“Wavelet transform based watermark for digital images,”
Xiang-Gen Xia, Charles G Boncelet, and Gonzalo R Arce, · 1998
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“Image quality assessment: from error visibility to structural similarity,”
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli, · 2004
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“Reversible data hiding,”
Zhicheng Ni, Yun-Qing Shi, Nirwan Ansari, and Wei Su, · 2006
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Digital watermarking and steganography
Ingemar Cox, Matthew Miller, Jeffrey Bloom, Jessica Fridrich, and Ton Kalker, · 2007
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“A constructive and unifying framework for zero-bit watermarking,”
Teddy Furon, · 2007
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“Optimal watermark embedding and detection strategies under limited detection resources,”
Neri Merhav and Erez Sabbag, · 2008
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“Imagenet: A large-scale hierarchical image database,”
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei, · 2009
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“Torchvision the machine-vision package of torch,”
Sébastien Marcel and Yann Rodriguez, · 2010
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“Negative evidences and co-occurences in image retrieval: The benefit of pca and whitening,”
Hervé Jégou and Ondřej Chum, · 2012
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“Intriguing properties of neural networks,”
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus, · 2013
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“Perceptual dft watermarking with improved detection and robustness to geometrical distortions,”
Matthieu Urvoy, Dalila Goudia, and Florent Autrusseau, · 2014
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“Explaining and harnessing adversarial examples,”
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy, · 2014
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“Microsoft coco: Common objects in context,”
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick, · 2014
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“Adam: A method for stochastic optimization,”
Diederik P. Kingma and Jimmy Ba, · 2015
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“Yfcc100m: The new data in multimedia research,”
Bart Thomee, David A. Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li, · 2016
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“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
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“Exploring the learning capabilities of convolutional neural networks for robust image watermarking,”
Haribabu Kandi, Deepak Mishra, and Subrahmanyam R.K. Sai Gorthi, · 2017
“Robust watermarking using inverse gradient attention,”
Honglei Zhang, Hu Wang, Yuanzhouhan Cao, Chunhua Shen, and Yidong Li, · 2020
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“Bootstrap your own latent: A new approach to self-supervised learning,”
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al., · 2020
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“Convolutional neural network-based digital image watermarking adaptive to the resolution of image and watermark,”
Jae-Eun Lee, Young-Ho Seo, and Dong-Wook Kim, · 2020
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“Distortion agnostic deep watermarking,”
Xiyang Luo, Ruohan Zhan, Huiwen Chang, Feng Yang, and Peyman Milanfar, · 2020
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“Attention based data hiding with generative adversarial networks,”
Chong Yu, · 2020
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Cited alongside, same era.
“Hidden: Hiding data with deep networks,”
Jiren Zhu, Russell Kaplan, Justin Johnson, and Li Fei-Fei, · 2018
Cited alongside, same era.
“Are deep neural networks good for blind image watermarking?,”
Vedran Vukotić, Vivien Chappelier, and Teddy Furon, · 2018
Cited alongside, same era.
“The unreasonable effectiveness of deep features as a perceptual metric,”
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang, · 2018
Cited alongside, same era.
“Romark: A robust watermarking system using adversarial training,”
Bingyang Wen and Sergul Aydore, · 2019
Cited alongside, same era.
“Watermarking error exponents in the presence of noise: The case of the dual hypercone detector,”
Teddy Furon, · 2019
Cited alongside, same era.
“Are classification deep neural networks good for blind image watermarking?,”
Vedran Vukotić, Vivien Chappelier, and Teddy Furon, · 2020
Cited alongside, same era.
“Crosstransformers: spatially-aware few-shot transfer,”
Carl Doersch, Ankush Gupta, and Andrew Zisserman, · 2020
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“A simple framework for contrastive learning of visual representations,”
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton, · 2020
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“Momentum contrast for unsupervised visual representation learning,”
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick, · 2020
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“Understanding contrastive representation learning through alignment and uniformity on the hypersphere,”
Tongzhou Wang and Phillip Isola, · 2020
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“Emerging properties in self-supervised vision transformers,”
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin, · 2021
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“Data hiding with deep learning: A survey unifying digital watermarking and steganography,”
Olivia Byrnes, Wendy La, Hu Wang, Congbo Ma, Minhui Xue, and Qi Wu, · 2021
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“Barlow twins: Self-supervised learning via redundancy reduction,”
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny, · 2021
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“Augly: A data augmentations library for audio, image, text, and video,” 2021
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