Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models

Language Models are Few-Shot Learners

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works

Learning Transferable Visual Models From Natural Language Supervision

Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293 · see citing works

Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics

Kaugeranna, R., Kaugeranna, E., & Aion, (. S. 4. 6. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 13,695 · see citing works

Denoising Diffusion Probabilistic Models

Ho, J., Jain, A., & Abbeel, P. (2020) · arXiv · doi:10.48550/arxiv.2006.11239 · Cited by 8,712 · see citing works

On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · cited 1× · see citing works

Semantics derived automatically from language corpora contain human-like biases

Caliskan, A., Bryson, J. J., & Narayanan, A. (2017) · Science · doi:10.1126/science.aal4230 · Cited by 2,887 · see citing works

Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., et al. (2022) · arXiv · doi:10.48550/arxiv.2205.11487 · Cited by 2,528 · see citing works

Hierarchical Text-Conditional Image Generation with CLIP Latents

Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022) · arXiv · doi:10.48550/arxiv.2204.06125 · Cited by 2,511 · cited 5× · see citing works

Datasheets for Datasets

Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2018) · arXiv · doi:10.48550/arxiv.1803.09010 · Cited by 2,474

Datasheets for datasets

Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., III, H. D., & Crawford, K. (2021) · Communications of the ACM · doi:10.1145/3458723 · Cited by 2,469 · see citing works

Language Models as Knowledge Bases?

Petroni, F., Rocktäschel, T., Riedel, S., Lewis, P., Bakhtin, A., Wu, Y., & Miller, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1250 · Cited by 1,834 · see citing works

Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016) · arXiv · doi:10.48550/arxiv.1607.06520 · Cited by 1,615 · see citing works

GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., & Chen, M. (2021) · arXiv · doi:10.48550/arxiv.2112.10741 · Cited by 1,357 · see citing works

Classifier-Free Diffusion Guidance

Ho, J., & Salimans, T. (2022) · arXiv · doi:10.48550/arxiv.2207.12598 · Cited by 1,126 · see citing works

LAION-5B: An open large-scale dataset for training next generation image-text models

Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al. (2022) · arXiv · doi:10.48550/arxiv.2210.08402 · Cited by 1,022 · see citing works

Imagic: Text-Based Real Image Editing with Diffusion Models

Kawar, B., Zada, S., Lang, O., Tov, O., Chang, H., Dekel, T., Mosseri, I., & Irani, M. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00582 · Cited by 813 · see citing works

RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models

Gehman, S., Gururangan, S., Sap, M., Choi, Y., & Smith, N. A. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.301 · Cited by 693 · see citing works

Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis

Larrazabal, A. J., Nieto, N., Peterson, V., Milone, D. H., & Ferrante, E. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1919012117 · Cited by 685 · see citing works

How Much Knowledge Can You Pack into the Parameters of a Language Model?

Roberts, A., Raffel, C., & Shazeer, N. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.437 · Cited by 638 · see citing works

Prompt-to-Prompt Image Editing with Cross Attention ControlPreprint

Hertz, A., Mokady, R., Tenenbaum, J., Aberman, K., Pritch, Y., & Cohen‐Or, D. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2208.01626 · Cited by 496 · see citing works

Mitigating Gender Bias in Natural Language Processing: Literature Review

Sun, T., Gaut, A., Tang, S., Huang, Y., ElSherief, M., Zhao, J., Mirza, D., Belding, E., Chang, K.-W., & Wang, W. Y. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1159 · Cited by 492 · see citing works

Persistent Anti-Muslim Bias in Large Language Models

Abid, A., Farooqi, M., & Zou, J. (2021) · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3461702.3462624 · Cited by 476 · see citing works

Measurement and Fairness

Jacobs, A. Z., & Wallach, H. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445901 · Cited by 362 · see citing works

LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs

Schuhmann, C., Vencu, R., Beaumont, R., Kaczmarczyk, R., Mullis, C., Katta, A., Coombes, T., Jitsev, J., & Komatsuzaki, A. (2021) · arXiv · doi:10.48550/arxiv.2111.02114 · Cited by 358 · see citing works

On Aliased Resizing and Surprising Subtleties in GAN Evaluation

Parmar, G., Zhang, R., & Zhu, J.-Y. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01112 · Cited by 307 · see citing works

Large pre-trained language models contain human-like biases of what is right and wrong to do

Schramowski, P., Turan, C., Andersen, N., Rothkopf, C. A., & Kersting, K. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00458-8 · Cited by 291 · see citing works

Large image datasets: A pyrrhic win for computer vision?

Birhane, A., & Prabhu, V. U. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00158 · Cited by 257 · see citing works

Reporting bias and knowledge acquisition

Gordon, J., & Durme, B. V. (2013) · Conference on Automated Knowledge Base Construction · doi:10.1145/2509558.2509563 · Cited by 240 · see citing works

Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP

Schick, T., Udupa, S., & Schütze, H. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00434 · Cited by 227 · see citing works

Text2LIVE: Text-Driven Layered Image and Video Editing

Bar-Tal, O., Ofri-Amar, D., Fridman, R., Kasten, Y., & Dekel, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19784-0_41 · Cited by 222 · see citing works

On the genealogy of machine learning datasets: A critical history of ImageNet

Denton, E., Hanna, A., Amironesei, R., Smart, A., & Nicole, H. (2021) · Big Data & Society · doi:10.1177/20539517211035955 · Cited by 221 · see citing works

Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases

Steed, R., & Caliskan, A. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445932 · Cited by 167 · see citing works

Multimodal datasets: misogyny, pornography, and malignant stereotypes

Birhane, A., Prabhu, V. U., & Kahembwe, E. (2021) · arXiv · doi:10.48550/arxiv.2110.01963 · Cited by 160 · see citing works

Why Yellow Fever Isn't Flattering: A Case Against Racial Fetishes

ZHENG, R. (2016) · Journal of the American Philosophical Association · doi:10.1017/apa.2016.25 · Cited by 125 · see citing works

Robo-writers: the rise and risks of language-generating AI

Hutson, M. (2021) · Nature · doi:10.1038/d41586-021-00530-0 · Cited by 106 · see citing works

The Socio-Moral Image Database (SMID): A novel stimulus set for the study of social, moral and affective processes

Crone, D., Bode, S., Murawski, C., & Laham, S. (2018) · PLoS ONE · doi:10.1371/journal.pone.0190954 · Cited by 77 · see citing works

REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets

Wang, A., Narayanan, A., & Russakovsky, O. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58580-8_43 · Cited by 72 · see citing works

Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content?

Schramowski, P., Tauchmann, C., & Kersting, K. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533192 · Cited by 63 · see citing works

Semantics Derived Automatically from Language Corpora Contain Human-like Moral Choices

Jentzsch, S. F., Schramowski, P., Rothkopf, C. A., & Kersting, K. (2019) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3306618.3314267 · Cited by 58 · see citing works

Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations

Karako, C., & Manggala, P. (2018) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3213586.3226206 · Cited by 54 · see citing works

The Moral Choice Machine

Schramowski, P., Turan, C., Jentzsch, S. F., Rothkopf, C. A., & Kersting, K. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.00036 · Cited by 33 · see citing works

Scalable Detection of Offensive and Non-compliant Content / Logo in Product Images

Gandhi, S., Kokkula, S., Chaudhuri, A., Magnani, A., Stanley, T., Ahmadi, B., Kandaswamy, V., Ovenc, O., & Mannor, S. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093454 · Cited by 32 · see citing works

Reflections on researching pornography across Asia: voices from the region

Jacobs, K., Baudinette, T., & Hambleton, A. (2020) · Porn Studies · doi:10.1080/23268743.2020.1776152 · Cited by 6 · see citing works

Diffusion Models: A Comprehensive Survey of Methods and Applications

Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Zhang, W., Cui, B., & Yang, M.-H. (2024) · ACM Computing Surveys · doi:10.1145/3626235 · Cited by 1,704 · see citing works

Erasing Concepts from Diffusion Models

Gandikota, R., Materzyńska, J., Fiotto-Kaufman, J., & Bau, D. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00230 · Cited by 250 · see citing works

Ablating Concepts in Text-to-Image Diffusion Models

Kumari, N., Zhang, B., Wang, S.-Y., Shechtman, E., Zhang, R., & Zhu, J.-Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02074 · Cited by 150 · see citing works

Diffusion Model-Based Image Editing: A Survey

Huang, Y., Huang, J., Liu, Y., Yan, M., Lv, J., Liu, J., Xiong, W., Zhang, H., Cao, L., & Chen, S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3541625 · Cited by 168 · see citing works

Unified Concept Editing in Diffusion Models

Gandikota, R., Orgad, H., Belinkov, Y., Materzyńska, J., & Bau, D. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00503 · Cited by 127 · see citing works

Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models

Zhang, G., Wang, K., Xu, X., Wang, Z., & Shi, H. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00182 · Cited by 115 · see citing works

Federated Recommender System Based on Diffusion Augmentation and Guided Denoising

Di, Y., Shi, H., Wang, X., Ma, R., & Liu, Y. (2024) · ACM Transactions on Information Systems · doi:10.1145/3688570 · Cited by 102 · see citing works

Comprehensive exploration of diffusion models in image generation: a survey

Chen, H., Xiang, Q., Hu, J., Ye, M., Yu, C., Cheng, H., & Zhang, L. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11110-3 · Cited by 104 · see citing works

LEDITS++: Limitless Image Editing Using Text-to-Image Models

Brack, M., Friedrich, F., Kornmeier, K., Tsaban, L., Schramowski, P., Kersting, K., & Passos, A. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00846 · Cited by 93 · see citing works

The Revolution of Multimodal Large Language Models: A Survey

Caffagni, D., Cocchi, F., Barsellotti, L., Moratelli, N., Sarto, S., Baraldi, L., Baraldi, L., Cornia, M., & Cucchiara, R. (2024) · Findings of the Association for Computational Linguistics ACL 2024 · doi:10.18653/v1/2024.findings-acl.807 · Cited by 98 · see citing works

MACE: Mass Concept Erasure in Diffusion Models

Lu, S., Wang, Z., Li, L., Liu, Y., & Adams Wai Kin, K. O. N. G. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00615 · Cited by 90 · see citing works

Security and Privacy on Generative Data in AIGC: A Survey

Wang, T., Zhang, Y., Qi, S., Zhao, R., Xia, Z., & Weng, J. (2024) · ACM Computing Surveys · doi:10.1145/3703626 · Cited by 105 · see citing works

Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models

Qu, Y., Shen, X., He, X., Michael Backes, A., Zannettou, S., & Zhang, Y. (2023) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3576915.3616679 · Cited by 87 · see citing works

Advances in diffusion models for image data augmentation: a review of methods, models, evaluation metrics and future research directions

Alimisis, P., Mademlis, I., Grammatikis, P. I. R., Sarigiannidis, P. G., & Papadopoulos, G. T. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11116-x · Cited by 77 · see citing works

MMA-Diffusion: MultiModal Attack on Diffusion Models

Yang, Y., Gao, R., Wang, X., Ho, T.-Y., Xu, N., & Xu, Q. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00739 · Cited by 69 · see citing works

To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy to Generate Unsafe Images ... For Now

Zhang, Y., Jia, J., Chen, X., Chen, A., Zhang, Y., Liu, S., Ding, K., & Liu, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72998-0_22 · Cited by 68 · see citing works

NetDiffus: Network traffic generation by diffusion models through time-series imaging

Sivaroopan, N., Bandara, D., Madarasingha, C., Jourjon, G., Jayasumana, A. P., & Thilakarathna, K. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110616 · Cited by 65 · see citing works

Auditing and instructing text-to-image generation models on fairness

Friedrich, F., Brack, M., Struppek, L., Hintersdorf, D., Schramowski, P., Luccioni, A. S., & Kersting, K. (2024) · AI and Ethics · doi:10.1007/s43681-024-00531-5 · Cited by 47 · see citing works

Let the Chart Spark: Embedding Semantic Context into Chart with Text-to-Image Generative Model

Xiao, S., Huang, S., Lin, Y., Ye, Y., & Zeng, W. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3326913 · Cited by 44 · see citing works

Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art

Chakraborty, N., Ornik, M., & Driggs-Campbell, K. (2025) · ACM Computing Surveys · doi:10.1145/3716846 · Cited by 49 · see citing works

Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesis

Struppek, L., Hintersdorf, D., Friedrich, F., Br, M., Schramowski, P., & Kersting, K. (2023) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.15388 · Cited by 48 · see citing works

Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models

Gandikota, R., Materzyńska, J., Zhou, T., Torralba, A. B., & Bau, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73661-2_10 · Cited by 54 · see citing works

Parents and Children: Distinguishing Multimodal Deepfakes from Natural Images

Amoroso, R., Morelli, D., Cornia, M., Baraldi, L., Bimbo, A. D., & Cucchiara, R. (2024) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3665497 · Cited by 42 · see citing works

Image Generative AI to Design Public Spaces: a Reflection of How AI Could Improve Co-Design of Public Parks

Guridi, J. A., Cheyre, C., Goula, M., Santo, D., Humphreys, L., Souras, A., & Shankar, A. (2024) · Digital Government Research and Practice · doi:10.1145/3656588 · Cited by 34 · see citing works

Diffusion Models Without Attention

Yan, J. N., Gu, J., & Rush, A. M. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00787 · Cited by 37 · see citing works

Adversarial attacks and defenses on text-to-image diffusion models: A survey

Zhang, C., Hu, M., Li, W., & Wang, L. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102701 · Cited by 33 · see citing works

Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image Generation

Li, H., Shen, C., Torr, P. H. S., Tresp, V., & Gu, J. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01141 · Cited by 34 · see citing works

One-dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications

Lyu, M., Yang, Y., Hong, H., Chen, H., Jin, X., He, Y., Xue, H., Han, J., & Ding, G. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00722 · Cited by 33 · see citing works

Reliable and Efficient Concept Erasure of Text-to-Image Diffusion Models

Gong, C., Chen, K., Wei, Z., Chen, J., & Jiang, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73668-1_5 · Cited by 34 · see citing works

Receler: Reliable Concept Erasing of Text-to-Image Diffusion Models via Lightweight Erasers

Huang, C.-P., Chang, K.-P., Tsai, C.-T., Lai, Y.-H., Yang, F.-E., & Wang, Y.-C. F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73661-2_20 · Cited by 33 · see citing works

Balancing Act: Distribution-Guided Debiasing in Diffusion Models

Parihar, R., Bhat, A., Basu, A., Mallick, S., Kundu, J. N., & Babu, R. V. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00637 · Cited by 26 · see citing works

T2IAT: Measuring Valence and Stereotypical Biases in Text-to-Image Generation

Wang, J., Liu, X., Di, Z., Liu, Y., & Wang, X. E. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.160 · Cited by 26 · see citing works

Latent Guard: A Safety Framework for Text-to-Image Generation

Liu, R., Khakzar, A., Gu, J., Chen, Q., Torr, P. H. S., & Pizzati, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73347-5_6 · Cited by 25 · see citing works

SafeGen: Mitigating Sexually Explicit Content Generation in Text-to-Image Models

Li, X., Yang, Y., Deng, J., Yan, C., Chen, Y., Ji, X., & Xu, W. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3670295 · Cited by 28 · see citing works

On the use of Stable Diffusion for creating realistic faces: from generation to detection

Papa, L., Faiella, L., Corvitto, L., Maiano, L., & Amerini, I. (2023) · International Workshop on Biometrics and Forensics (IWBF) · doi:10.1109/iwbf57495.2023.10156981 · Cited by 22 · see citing works

From Fake Perfects to Conversational Imperfects: Exploring Image-Generative AI as a Boundary Object for Participatory Design of Public Spaces

Guridi, J. A., Hwang, A. H., Santo, D., Goula, M., Cheyre, C., Humphreys, L., & Rangel, M. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3710912 · Cited by 24 · see citing works

Playgrounds and Prejudices: Exploring Biases in Generative AI For Children.

Baines, A., Gruia, L., Collyer-Hoar, G., & Rubegni, E. (2024) · Proceedings of the 23rd Annual ACM Interaction Design and Children Conference · doi:10.1145/3628516.3659404 · Cited by 21 · see citing works

Beyond Numbers: Creating Analogies to Enhance Data Comprehension and Communication with Generative AI

Chen, Q., Shuai, W., Zhang, J., Sun, Z., & Cao, N. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642480 · Cited by 20 · see citing works

A Survey on Quality Metrics for Text-to-Image Generation

Hartwig, S., Engel, D., Sick, L., Kniesel, H., Payer, T., Poonam, P., Glöckler, M., Bäuerle, A., & Ropinski, T. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3585077 · Cited by 39 · see citing works

Safe-CLIP: Removing NSFW Concepts from Vision-and-Language Models

Poppi, S., Poppi, T., Cocchi, F., Cornia, M., Baraldi, L., & Cucchiara, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73668-1_20 · Cited by 22 · see citing works

A Survey of Multimodal Controllable Diffusion Models

Jiang, R., Zheng, G., Li, T., Yang, T., Wang, J., & Li, X. (2024) · Journal of Computer Science and Technology · doi:10.1007/s11390-024-3814-0 · Cited by 17 · see citing works

Situating the social issues of image generation models in the model life cycle: a sociotechnical approach

Katirai, A., García, N., Ide, K., Nakashima, Y., & Kishimoto, A. (2024) · AI and Ethics · doi:10.1007/s43681-024-00517-3 · Cited by 17 · see citing works

R.A.C.E. : Robust Adversarial Concept Erasure for Secure Text-to-Image Diffusion Model

Kim, C., Min, K., & Yang, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73010-8_27 · Cited by 14 · see citing works

Erasing Undesirable Influence in Diffusion Models

Wu, J., Le, T., Hayat, M., & Harandi, M. T. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02632 · Cited by 13 · see citing works

VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models

Huang, Z., Zhang, F., Xu, X., He, Y., Yu, J., Dong, Z., Ma, Q., Chanpaisit, N., Si, C., Jiang, Y., et al. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3633890 · Cited by 21 · see citing works

Degeneration-Tuning: Using Scrambled Grid shield Unwanted Concepts from Stable Diffusion

Ni, Z., Wei, L., Li, J., Tang, S., Zhuang, Y., & Tian, Q. C. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611867 · Cited by 13 · see citing works

SurrogatePrompt: Bypassing the Safety Filter of Text-to-Image Models via Substitution

Ba, Z., Zhong, J., Lei, J., Cheng, P., Wang, Q., Qin, Z., Wang, Z., & Ren, K. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3690346 · Cited by 13 · see citing works

Selective, Interpretable and Motion Consistent Privacy Attribute Obfuscation for Action Recognition

Ilic, F., Zhao, H., Pock, T., & Wildes, R. P. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01772 · Cited by 12 · see citing works

MarkPlugger: Generalizable Watermark Framework for Latent Diffusion Models Without Retraining

Zhang, G., Wang, L., Su, Y., & Liu, A.-A. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3565960 · Cited by 11 · see citing works

Precise, Fast, and Low-cost Concept Erasure in Value Space: Orthogonal Complement Matters

Wang, Y., Li, O., Mu, T., Hao, Y., Liu, K., Wang, X., & He, X. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02678 · Cited by 12 · see citing works

Now you see me, now you don’t: an exploration of religious exnomination in DALL-E

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T2ISafety: Benchmark for Assessing Fairness, Toxicity, and Privacy in Image Generation

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Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-to-Image Generation Models

Dong, Y., Meng, X., Yu, N., Li, Z., & Guo, S. (2025) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp61157.2025.00119 · Cited by 8 · see citing works

Decoupled Distillation to Erase: A General Unlearning Method for Any Class-centric Tasks

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Scissorhands: Scrub Data Influence via Connection Sensitivity in Networks

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A Survey of Machine Unlearning in Generative AI Models: Methods, Applications, Security, and Challenges

Huang, A., Cai, Z., & Xiong, Z. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3570989 · Cited by 7 · see citing works

MeshUp: Multi-Target Mesh Deformation via Blended Score Distillation

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Localization and Manipulation of Immoral Visual Cues for Safe Text-to-Image Generation

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Symmetry-Aware Advances in Multimodal Large Language Models: Architectures, Training, and Evaluation

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Selectively Informative Description can Reduce Undesired Embedding Entanglements in Text-to-Image Personalization

Kim, J., Park, J., & Rhee, W. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00794 · Cited by 5 · see citing works

On the Trustworthiness Landscape of State-of-the-art Generative Models: A Survey and Outlook

Fan, M., Wang, C., Chen, C., Liu, Y., & Huang, J. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02375-w · Cited by 17 · see citing works

Debiasing Text-to-Image Diffusion Models

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Implicit Concept Removal of Diffusion Models

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Mitigating Social Biases in Text-to-Image Diffusion Models via Linguistic-Aligned Attention Guidance

Jiang, Y., Lyu, Y., He, Z., Peng, B., & Dong, J. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680748 · Cited by 5 · see citing works

Image-Perfect Imperfections: Safety, Bias, and Authenticity in the Shadow of Text-To-Image Model Evolution

Wu, Y., Shen, Y., Michael Backes, A., & Zhang, Y. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3690288 · Cited by 5 · see citing works

InvDiff: Invariant Guidance for Bias Mitigation in Diffusion Models

Hou, M., Wu, Y., Xu, C., Huang, Y.-H., Bai, C., Wu, L., & Bian, J. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709165 · Cited by 7 · see citing works

Set You Straight: Auto-Steering Denoising Trajectories to Sidestep Unwanted Concepts

Li, L., Lu, S., Ren, Y., & Kong, A. W. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3754546 · Cited by 5 · see citing works

Localized Concept Erasure for Text-to-Image Diffusion Models Using Training-Free Gated Low-Rank Adaptation

Lee, B. H., Lim, S., & Chun, S. Y. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01733 · Cited by 4 · see citing works

Evaluating the Social Impact of Generative AI Systems

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Modifier Unlocked: Jailbreaking Text-to-Image Models Through Prompts

Liu, S., Ma, M., Xue, M., & Bai, G. (2025) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp61157.2025.00242 · Cited by 4 · see citing works

ACE: Anti-Editing Concept Erasure in Text-to-Image Models

Wang, Z., Wei, Y., Li, F., Pei, R., Xu, S., & Zuo, W. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02189 · Cited by 4 · see citing works

Data Redaction from Conditional Generative Models

Kong, Z., & Chaudhuri, K. (2024) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml59370.2024.00035 · Cited by 4 · see citing works

Legibility Diffuser: Offline Imitation for Intent Expressive Motion

Bronars, M., Cheng, S., & Xu, D. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3421849 · Cited by 4 · see citing works

IMMA: Immunizing Text-to-Image Models Against Malicious Adaptation

Zheng, A. Y., & Yeh, R. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72933-1_26 · Cited by 4 · see citing works

Coarse-to-Fine Latent Diffusion Model for Glaucoma Forecast on Sequential Fundus Images

Zhang, Y., Huang, K., Yang, X., Ma, X., Wu, J., Wang, N., Wang, X., & Heng, P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72086-4_16 · Cited by 4 · see citing works

Unified Prompt Attack Against Text-to-Image Generation Models

Peng, D., Ke, Q., Huang, H., Hu, P., & Liu, J. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3545652 · Cited by 7 · see citing works

Espresso: Robust Concept Filtering in Text-to-Image Models

Das, A., Duddu, V., Zhang, R., & Asokan, N. (2024) · ACM Conference on Data and Application Security and Privacy (CODASPY) · doi:10.1145/3714393.3726502 · Cited by 4 · see citing works

NDM: A Noise-driven Detection and Mitigation Framework against Implicit Sexual Intentions in Text-to-Image Generation

Sun, Y., Huang, Y., Zhang, R., Chen, H., Ruan, S., Duan, R., & Wei, X. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755326 · Cited by 3 · see citing works

Responsible Visual Editing

Ni, M., Shen, Y., Lei Zhang, A., & Zuo, W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72670-5_18 · Cited by 3 · see citing works

Unbiased-Diff: Analyzing and Mitigating Biases in Diffusion Model-Based Face Image Generation

Perera, M. V., & Patel, V. M. (2025) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2024.3525037 · Cited by 3 · see citing works

FareMark: Model-Watermark-Driven Free-Rider Detection in Federated Learning Model

Li, L., Zhang, X., Wu, H., Feng, G., & Zhang, W. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3587745 · Cited by 3 · see citing works

STEPS: Sequential Probability Tensor Estimation for Text-to-Image Hard Prompt Search

Qiu, Y., Wang, A., Li, C., Huang, H., Zhou, G., & Zhao, Q. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02667 · Cited by 3 · see citing works

Distilling Adversarial Prompts from Safety Benchmarks: Report for the Adversarial Nibbler Challenge

Brack, M., Schramowski, P., & Kristian Kersting, B. (2023) · doi:10.18653/v1/2023.artofsafety-1.3 · Cited by 3 · see citing works

Concept Arithmetics for Circumventing Concept Inhibition in Diffusion Models

Petsiuk, V., & Saenko, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73223-2_18 · Cited by 3 · see citing works

OmniFusion: Exemplar-Based Video Colorization Using OmniMotion and DifFusion Priors

Fang, X., Dai, L., & Tang, J. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0917-8_5 · Cited by 3 · see citing works

Diffusion-based Identity-Preserving Facial Privacy Protection

Han, D. K., Mohamed, S., Li, Y., & Denzler, J. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890829 · Cited by 3 · see citing works

ShieldDiff: Suppressing Sexual Content Generation from Diffusion Models through Reinforcement LearningPreprint

Han, D. K., Mohamed, S., & Li, Y. (2024) · Preprints.org · doi:10.20944/preprints202407.0330.v1 · Cited by 5 · see citing works

AlignGuard: Scalable Safety Alignment for Text-to-Image Generation

Liu, R., Chen, I. C., Gu, J., Zhang, J., Pi, R., Chen, Q., Torr, P. H. S., Khakzar, A., & Pizzati, F. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01581 · Cited by 2 · see citing works

Learning to Unlearn While Retaining: Combating Gradient Conflicts in Machine Unlearning

Patel, G., & Qiu, Q. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00401 · Cited by 2 · see citing works

Embedding an Ethical Mind: Aligning Text-to-Image Synthesis via Lightweight Value Optimization

Wang, X., Yi, X., Xie, X., & Jia, J. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681652 · Cited by 2 · see citing works

Safeguard Text-to-Image Diffusion Models with Human Feedback Inversion

Kim, S., Jung, S., Kim, B., Choi, M., Shin, J., & Lee, J.-H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72855-6_8 · Cited by 2 · see citing works

Improving Geo-Diversity of Generated Images with Contextualized Vendi Score Guidance

Hemmat, R. A., Hall, M. A., Sun, A., Ross, C., Drozdzal, M., & Romero, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73021-4_13 · Cited by 2 · see citing works

A Systematic Review of Open Datasets Used in Text-to-Image (T2I) Gen AI Model Safety

Bavalatti, T., Ahmed, O., Potdar, D., Rouf, R., Jawed, F., Govind, M., & Krishnan, S. (2025) · IEEE Access · doi:10.1109/access.2025.3539933 · Cited by 3 · see citing works

PromptGuard: Soft Prompt-Guided Unsafe Content Moderation for Text-to-Image Models

Yuan, L., Li, X., Xu, C., Tao, G., Jia, X., Huang, Y., Dong, W., Liu, Y., & Li, B. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3678787 · Cited by 2 · see citing works

Moderator: Moderating Text-to-Image Diffusion Models through Fine-grained Context-based Policies

Wang, P., Li, Q., Yu, L., Wang, Z., Li, A., & Jin, H. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3690327 · Cited by 2 · see citing works

Enhancing Generative Class Incremental Learning Performance With a Model Forgetting Approach

Togo, T., Togo, R., Maeda, K., Ogawa, T., & Haseyama, M. (2025) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2025.3528900 · Cited by 1 · see citing works

Ethical-Lens: Curbing malicious usages of open-source text-to-image models

Cai, Y., Yin, S., Wei, Y., Xu, C., Mao, W., Juefei-Xu, F., Chen, S., & Wang, Y. (2025) · Patterns · doi:10.1016/j.patter.2025.101187 · Cited by 1 · see citing works

GENIE: Socially Unbiased Generative Text-to-Image Editing

Lau, J. K., Phan, R. C. ‐., Rajanala, S., Cox, I. J., & Pal, A. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888322 · Cited by 1 · see citing works

Exploring the Boundaries of Content Moderation in Text-to-Image Generation

Riccio, P., Curto, G., & Oliver, N. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92089-9_11 · Cited by 3 · see citing works

Varif.ai to Vary and Verify User-Driven Diversity in Scalable Image Generation

Michelessa, M., Ng, J., Hurter, C., & Lim, B. Y. (2025) · Designing Interactive Systems Conference · doi:10.1145/3715336.3735847 · Cited by 1 · see citing works

Training-Free Safe Text Embedding Guidance for Text-to-Image Diffusion ModelsPreprint

Na, B., Kang, M., Kwak, J., Park, M., Shin, J., Jun, S., Lee, G., Kim, J.-H., & Moon, I.-C. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2510.24012

Test-Time Scaling for Safe Text-Guided Image Generation via Intermediate Clean EstimatesPreprint

Sakurai, J., Yan, S., & Xu, X. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2608.03284

A modular approach to enhancing safety in text-to-image diffusion models via targeted LoRA fine-tuning

Kizitskyi, M., & Turuta, O. (2025) · doi:10.31110/colins/2025-2/001

Explicit Content Generation in Text-to-Image Models: Training Mechanisms, Moderation Failures, and Safety Limits

Yajnesh, R. (2026) · Indian Journal of Computer Science and Technology · doi:10.59256/indjcst.20260502111

Beautiful Images, Toxic Words: Understanding and Addressing Offensive Text in Generated Images

Kumar, A., Blanchard, T., Dziedzic, A., & Boenisch, F. (2026) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v40i44.41086