{"total":20,"rows":[{"id":"2305.15241","title":"Robust Classification via a Single Diffusion Model","created":"2023-05-24","cited_by_count":79,"area":"diffusion"},{"id":"2405.16341","title":"R.A.C.E.: Robust Adversarial Concept Erasure for Secure Text-to-Image Diffusion Model","created":"2024-05-25","cited_by_count":58,"area":"diffusion"},{"id":"2303.09962","title":"Adversarial Counterfactual Visual Explanations","created":"2023-03-17","cited_by_count":47,"area":"diffusion"},{"id":"2208.08664","title":"Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance","created":"2022-08-18","cited_by_count":33,"area":"diffusion"},{"id":"2502.01189","title":"Compressed Image Generation with Denoising Diffusion Codebook Models","created":"2025-02-03","cited_by_count":28,"area":"diffusion"},{"id":"2207.08089","title":"Threat Model-Agnostic Adversarial Defense using Diffusion Models","created":"2022-07-17","cited_by_count":24,"area":"diffusion"},{"id":"2410.10437","title":"Towards Reliable Verification of Unauthorized Data Usage in Personalized Text-to-Image Diffusion Models","created":"2024-10-14","cited_by_count":20,"area":"diffusion"},{"id":"2303.08500","title":"The Devil's Advocate: Shattering the Illusion of Unexploitable Data using Diffusion Models","created":"2023-03-15","cited_by_count":19,"area":"diffusion"},{"id":"2403.18035","title":"Bidirectional Consistency Models","created":"2024-03-26","cited_by_count":18,"area":"diffusion"},{"id":"2211.14794","title":"Traditional Classification Neural Networks are Good Generators: They are Competitive with DDPMs and GANs","created":"2022-11-27","cited_by_count":9,"area":"diffusion"},{"id":"2310.16779","title":"Multi-scale Diffusion Denoised Smoothing","created":"2023-10-25","cited_by_count":9,"area":"diffusion"},{"id":"2210.09643","title":"Improving Adversarial Robustness by Contrastive Guided Diffusion Process","created":"2022-10-18","cited_by_count":8,"area":"diffusion"},{"id":"2501.18877","title":"Mitigating Sexual Content Generation via Embedding Distortion in Text-conditioned Diffusion Models","created":"2025-01-31","cited_by_count":7,"area":"diffusion"},{"id":"2402.08200","title":"Fine-Tuning Text-To-Image Diffusion Models for Class-Wise Spurious Feature Generation","created":"2024-02-13","cited_by_count":4,"area":"diffusion"},{"id":"2407.18658","title":"Adversarial Robustification via Text-to-Image Diffusion Models","created":"2024-07-26","cited_by_count":3,"area":"diffusion"},{"id":"2405.16260","title":"Enhancing Consistency-Based Image Generation via Adversarialy-Trained Classification and Energy-Based Discrimination","created":"2024-05-25","cited_by_count":2,"area":"diffusion"},{"id":"2404.10588","title":"Do Counterfactual Examples Complicate Adversarial Training?","created":"2024-04-16","cited_by_count":1,"area":"diffusion"},{"id":"2501.05359","title":"SC-Pro: Training-Free Framework for Defending Unsafe Image Synthesis Attack","created":"2025-01-09","cited_by_count":1,"area":"diffusion"},{"id":"2504.11034","title":"Defending Against Frequency-Based Attacks with Diffusion Models","created":"2025-04-15","cited_by_count":1,"area":"diffusion"},{"id":"2307.04081","title":"Score-based Conditional Generation with Fewer Labeled Data by Self-calibrating Classifier Guidance","created":"2023-07-09","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}