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A generative AI model can generate extremely realistic-looking content, posing growing challenges to the authenticity of information.
Robust template matching for affine resistant image watermarks
Shelby Pereira and Thierry Pun. 2000 · 2000
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. 2004 · 2004
Earlier work this paper cites.
Robust image watermarking based on multiband wavelets and empirical mode decomposition
Ning Bi, Qiyu Sun, Daren Huang, Zhihua Yang, and Jiwu Huang. 2007 · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database. In IEEE/CVF Conference on Computer Vision and Pattern Recognition
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009 · 2009
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2013 · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014 · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context. In European Conference on Computer Vision
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014 · 2014
Earlier work this paper cites.
Towards evaluating the robustness of neural networks. In IEEE Symposium on Security and Privacy
Nicholas Carlini and David Wagner. 2017 · 2017
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017 · 2017
Earlier work this paper cites.
On the suitability of lp-norms for creating and preventing adversarial examples. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
Mahmood Sharif, Lujo Bauer, and Michael K Reiter. 2018 · 2018
Earlier work this paper cites.
Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Annual Meeting of the Association for Computational Linguistics
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. 2018 · 2018
Earlier work this paper cites.
Hidden: Hiding data with deep networks. In European Conference on Computer Vision
Jiren Zhu, Russell Kaplan, Justin Johnson, and Li Fei-Fei. 2018 · 2018
Earlier work this paper cites.
Detecting photoshopped faces by scripting photoshop. In IEEE/CVF International Conference on Computer Vision
Sheng-Yu Wang, Oliver Wang, Andrew Owens, Richard Zhang, and Alexei A Efros. 2019 · 2019
Earlier work this paper cites.
Romark: A robust watermarking system using adversarial training
Bingyang Wen and Sergul Aydore. 2019 · 2019
Earlier work this paper cites.
Attributing fake images to gans: Learning and analyzing gan fingerprints. In IEEE/CVF International Conference on Computer Vision
Ning Yu, Larry S Davis, and Mario Fritz. 2019 · 2019
Earlier work this paper cites.
SteganoGAN: High capacity image steganography with GANs
Kevin Alex Zhang, Alfredo Cuesta-Infante, Lei Xu, and Kalyan Veeramachaneni. 2019 · 2019
Earlier work this paper cites.
Hopskipjumpattack: A query-efficient decision-based attack. In IEEE Symposium on Security and Privacy
Jianbo Chen, Michael I Jordan, and Martin J Wainwright. 2020 · 2020
Cited alongside, same era.
Leveraging frequency analysis for deep fake image recognition. In International Conference on Machine Learning
Joel Frank, Thorsten Eisenhofer, Lea Schönherr, Asja Fischer, Dorothea Kolossa, and Thorsten Holz. 2020 · 2020
Cited alongside, same era.
Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2020 · 2020
Cited alongside, same era.
Distortion agnostic deep watermarking. In IEEE/CVF Conference on Computer Vision and Pattern Recognition
Xiyang Luo, Ruohan Zhan, Huiwen Chang, Feng Yang, and Peyman Milanfar. 2020 · 2020
Cited alongside, same era.
Invisible watermark
Qingquan Wang and buley. 2020 · 2020
Cited alongside, same era.
Chatgpt: Optimizing language models for dialogue
OpenAI. 2022 · 2022
Later among the works it cites.
Stable Diffusion watermark decoder
Robin Rombach. 2022 · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
Later among the works it cites.
DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Diffusion Models
Zeyang Sha, Zheng Li, Ning Yu, and Yang Zhang. 2022 · 2022
Later among the works it cites.
US Copyright Office: AI Generated Works Are Not Eligible for Copyright
ARTnews. 2023 · 2023
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The Stable Signature: Rooting Watermarks in Latent Diffusion Models
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Matthew Tancik, Ben Mildenhall, and Ren Ng. 2020 · 2020
Cited alongside, same era.
Udh: Universal deep hiding for steganography, watermarking, and light field messaging
Chaoning Zhang, Philipp Benz, Adil Karjauv, Geng Sun, and In So Kweon. 2020a · 2020
Cited alongside, same era.
Chaoning Zhang, Adil Karjauv, Philipp Benz, and In So Kweon. 2020b · 2020
Cited alongside, same era.
Adversarial watermarking transformer: Towards tracing text provenance with data hiding. In IEEE Symposium on Security and Privacy
Sahar Abdelnabi and Mario Fritz. 2021 · 2021
Cited alongside, same era.
Zero-shot text-to-image generation. In International Conference on Machine Learning
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021 · 2021
Cited alongside, same era.
Faceguard: Proactive deepfake detection
Yuankun Yang, Chenyue Liang, Hongyu He, Xiaoyu Cao, and Neil Zhenqiang Gong. 2021 · 2021
Cited alongside, same era.
Artificial fingerprinting for generative models: Rooting deepfake attribution in training data. In IEEE/CVF International Conference on Computer Vision
Ning Yu, Vladislav Skripniuk, Sahar Abdelnabi, and Mario Fritz. 2021 · 2021
Cited alongside, same era.
Pierre Fernandez, Guillaume Couairon, Hervé Jégou, Matthijs Douze, and Teddy Furon. 2023 · 2023
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Meta’s powerful AI language model has leaked online
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A watermark for large language models
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Paraphrasing evades detectors of ai-generated text, but retrieval is an effective defense. In Advances in Neural Information Processing Systems
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Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D Manning, and Chelsea Finn. 2023 · 2023
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Elon Musk isn’t dating GM’s Mary Barra: he has this to say though on the photos
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