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In the evolving landscape of text-to-image (T2I) diffusion models, the remarkable capability to generate high-quality images from textual descriptions faces challenges with the potential misuse of reproducing sensitive content.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)
2009
Earlier work this paper cites.
2014
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. pp. 234–241. Springer (2015)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Carlini, N., Wagner, D.A.: Towards evaluating the robustness of neural networks. 2017 IEEE Symposium on Security and Privacy (SP) pp. 39–57 (2016), https://api.semanticscholar.org/CorpusID:2893830
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Advances in Neural Information Processing Systems. pp. 6626–6637 (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Pinto, L., Davidson, J., Sukthankar, R., Gupta, A.: Robust adversarial reinforcement learning. In: International Conference on Machine Learning. pp. 2817–2826. PMLR (2017)
2017
Earlier work this paper cites.
Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., Madry, A.: Robustness may be at odds with accuracy. arXiv: Machine Learning (2018), https://api.semanticscholar.org/CorpusID:52962648
2018
Earlier work this paper cites.
Bedapudi, P.: Nudenet: Neural nets for nudity classification, detection and selective censoring (12 2019)
2019
Earlier work this paper cites.
Zhang, H., Yu, Y., Jiao, J., Xing, E., Ghaoui, L.E., Jordan, M.: Theoretically principled trade-off between robustness and accuracy. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 97, pp. 7472–7482. PMLR (09–15 Jun 2019), https://proceedings.mlr.press/v97/zhang19p.html
2019
Earlier work this paper cites.
Zhang, K.A., Xu, L., Cuesta-Infante, A., Veeramachaneni, K.: Robust invisible video watermarking with attention (2019)
2019
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Howard, J., Gugger, S.: fastai: A layered api for deep learning. Inf. 11
2020
Earlier work this paper cites.
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 8110–8119 (2020)
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 12873–12883 (2021)
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
Kim, C., Ren, Y., Yang, Y.: Decentralized attribution of generative models. In: International Conference on Learning Representations (2021)
2021
Earlier work this paper cites.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
Earlier work this paper cites.
Wang, Z., Guo, H., Zhang, Z., Liu, W., Qin, Z., Ren, K.: Feature importance-aware transferable adversarial attacks. 2021 IEEE/CVF International Conference on Computer Vision (ICCV) pp. 7619–7628 (2021), https://api.semanticscholar.org/CorpusID:236493523
2021
Earlier work this paper cites.
Wu, X., Guo, W., Wei, H., Xing, X.: Adversarial policy training against deep reinforcement learning. In: 30th USENIX Security Symposium (USENIX Security 21). pp. 1883–1900 (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Yu, N., Skripniuk, V., Abdelnabi, S., Fritz, M.: Artificial fingerprinting for generative models: Rooting deepfake attribution in training data. In: Proceedings of the IEEE/CVF International conference on computer vision. pp. 14448–14457 (2021)
2021
Cited alongside, same era.
Yuan, Z., Zhang, J., Jia, Y., Tan, C., Xue, T., Shan, S.: Meta gradient adversarial attack. 2021 IEEE/CVF International Conference on Computer Vision (ICCV) pp. 7728–7737 (2021), https://api.semanticscholar.org/CorpusID:236956844
2021
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Kumari, N., Zhang, B., Wang, S.Y., Shechtman, E., Zhang, R., Zhu, J.Y.: Ablating concepts in text-to-image diffusion models. 2023 IEEE/CVF International Conference on Computer Vision (ICCV) pp. 22634–22645 (2023), https://api.semanticscholar.org/CorpusID:257687839
2023
Later among the works it cites.
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2022
Cited alongside, same era.
Ho, J., Salimans, T.: Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)
2022
Cited alongside, same era.
Kinfu, K.A., Vidal, R.: Analysis and extensions of adversarial training for video classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3416–3425 (2022)
2022
Cited alongside, same era.
Kumari, N., Zhang, B., Zhang, R., Shechtman, E., Zhu, J.Y.: Multi-concept customization of text-to-image diffusion. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 1931–1941 (2022), https://api.semanticscholar.org/CorpusID:254408780
2022
Cited alongside, same era.
Nichol, A.Q., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., Mcgrew, B., Sutskever, I., Chen, M.: Glide: Towards photorealistic image generation and editing with text-guided diffusion models. In: International Conference on Machine Learning. pp. 16784–16804. PMLR (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 10684–10695 (June 2022)
2022
Cited alongside, same era.
Li, A.C., Prabhudesai, M., Duggal, S., Brown, E., Pathak, D.: Your diffusion model is secretly a zero-shot classifier. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 2206–2217 (October 2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Marcelo, P.: Fact focus: Fake image of pentagon explosion briefly sends jitters through stock market. Associated Press (May 2023), https://apnews.com/article/pentagon-explosion-fake-image-stock-market-jitters-78d5913603bdf9f17cc7a8c77a72e59b
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Wei, Y., Zhang, Y., Ji, Z., Bai, J., Zhang, L., Zuo, W.: Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation. 2023 IEEE/CVF International Conference on Computer Vision (ICCV) pp. 15897–15907 (2023), https://api.semanticscholar.org/CorpusID:257219968
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
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2023
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2023
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2023
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2024
Closest in time.
OpenAI: Chatgpt. Online (2022), https://chat.openai.com/chat , accessed on February 24, 2024
2024
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2024
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Pham, M., Marshall, K.O., Cohen, N., Mittal, G., Hegde, C.: Circumventing concept erasure methods for text-to-image generative models. In: The Twelfth International Conference on Learning Representations (2024), https://openreview.net/forum?id=ag3o2T51Ht
2024
Closest in time.
Wen, Y., Jain, N., Kirchenbauer, J., Goldblum, M., Geiping, J., Goldstein, T.: Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery. Advances in Neural Information Processing Systems 36
2024
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