TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger | Paper Citation Record

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This paper cites In: Proceedings of the AAAI Conference on Artificial Intelligence.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the AAAI Conference on Artificial Intelligence

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This paper cites In: Advances in Neural Information Processing Systems.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Advances in Neural Information Processing Systems

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This paper cites IEEE Transactions on Knowledge and Data Engineering pp.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger IEEE Transactions on Knowledge and Data Engineering pp

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This paper cites In: Proceedings of the International Conference on Learning Representations (2020).

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the International Conference on Learning Representations (2020)

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: CVPR

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This paper cites Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Reference 6

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger Unresolved cited work

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: NeuRIPS

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This paper cites Diffusion Models in Vision: A Survey.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger Diffusion Models in Vision: A Survey

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This paper cites IEEE Transactions on Information Forensics and Security19, 6364–6376 (2024).https://doi.org/10.1109/TIFS.2024.3411936.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger IEEE Transactions on Information Forensics and Security19, 6364–6376 (2024).https://doi.org/10.1109/TIFS.2024.3411936

Reference 10

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This paper cites In: Advances in Neural Information Processing Systems.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Advances in Neural Information Processing Systems

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This paper cites BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

Reference 12

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This paper cites In: CVPR.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: CVPR

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

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This paper cites Advances in Neural Information Processing Systems30(2017).

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger Advances in Neural Information Processing Systems30(2017)

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This paper cites In: Advances in Neural Information Processing Systems.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Advances in Neural Information Processing Systems

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This paper cites In: Advances in Neural Information Processing Systems.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Advances in Neural Information Processing Systems

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This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

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This paper cites In: Proceedings of the IEEE/CVF International Conference on Computer Vision.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the IEEE/CVF International Conference on Computer Vision

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This paper cites In: Proceedings of the International Conference on Machine Learning (2014).

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the International Conference on Machine Learning (2014)

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger Unresolved cited work

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger Invisible Backdoor Attacks on Diffusion Models

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This paper cites In: Advances in Neural Information Processing Systems.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Advances in Neural Information Processing Systems

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This paper cites Proceedings of the Interna- tional Journal of Computer Vision pp.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger Proceedings of the Interna- tional Journal of Computer Vision pp

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

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This paper cites In: Proceedings of the IEEE/CVF International Conference on Computer Vision.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the IEEE/CVF International Conference on Computer Vision

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This paper cites In: Advances in Neural Information Processing Systems.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Advances in Neural Information Processing Systems

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 28

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This paper cites TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors

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This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the International Conference on Machine Learning

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the International Conference on Machine Learning

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Advances in Neural Information Processing Systems (2023)

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the International Conference on Machine Learning

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the International Conference on Learning Representations (2020) Title Suppressed Due to Excessive Length 17

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Pro- ceedings of the International Conference on Machine Learning

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the AAAI conference on artificial intelligence

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the International Conference on Learning Representations (2021)

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Advances in Neural Information Processing Systems

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceed- ings of the International Conference on Learning Representations (2021)

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the IEEE/CVF International Conference on Computer Vision

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Advances in Neural Information Processing Systems

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger ACM Computing Surveys57(8), 1–44 (2025)

Reference 44

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This paper cites PureDiffusion: Using Backdoor to Counter Backdoor in Generative Diffusion Models.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger PureDiffusion: Using Backdoor to Counter Backdoor in Generative Diffusion Models

Reference 45

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the IEEE Wireless Commu- nications and Networking Conference

Reference 46

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger A Dual-Purpose Framework for Backdoor Defense and Backdoor Amplification in Diffusion Models

Reference 47

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 48

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Advances in Neural Information Processing Systems (2023)

Reference 49

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger IEEE Transactions on Image Process- ing13(4), 600–612 (2004)

Reference 50

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the Interna- tional Conference on Learning Representations (2021)

Reference 51

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 52

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This paper cites In: Proceedings of the Interna- tional Conference on Learning Representations (2021).

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the Interna- tional Conference on Learning Representations (2021)

Reference 53

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TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models

Reference 54

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This paper cites ACM Computing Surveys56(4), 1–39 (2023).

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger ACM Computing Surveys56(4), 1–39 (2023)

Reference 55

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This paper cites In: Proceedings of the International Symposium on Research in Sttacks, Intrusions and Defenses.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the International Symposium on Research in Sttacks, Intrusions and Defenses

Reference 56

Resolution: unresolved; no resolver link, observed 2026-06-26T07:54:58.047670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df3f0d7a-a5df-4a1b-ab61-4ee99c271874 · outbound

This paper cites In: ACM Multimedia.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: ACM Multimedia

Reference 57

Resolution: unresolved; no resolver link, observed 2026-06-26T07:54:58.047670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5e2cb0d-4007-4f98-a75c-21bf4529b284 · outbound

This paper cites In: Proceedings of the IEEE International Conference on Data Mining.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger In: Proceedings of the IEEE International Conference on Data Mining

Reference 58

Resolution: unresolved; no resolver link, observed 2026-06-26T07:54:58.047670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b17d6276-0eb0-4afd-b5bd-c771b3df23b1 · outbound

This paper cites A Survey of Diffusion Models in Natural Language Processing.

TooBad: Backdoor Diffusion Models with Ultra-Low Poison Rate and Imperceptible Trigger A Survey of Diffusion Models in Natural Language Processing

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-07-04T11:39:46.315069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers