{"total":87,"rows":[{"id":"2311.17516","title":"MMA-Diffusion: MultiModal Attack on Diffusion Models","created":"2023-11-29","cited_by_count":194,"area":"diffusion"},{"id":"2303.15433","title":"Anti-DreamBooth: Protecting users from personalized text-to-image synthesis","created":"2023-03-27","cited_by_count":120,"area":"diffusion"},{"id":"2305.12683","title":"Mist: Towards Improved Adversarial Examples for Diffusion Models","created":"2023-05-22","cited_by_count":94,"area":"diffusion"},{"id":"2311.12832","title":"Toward effective protection against diffusion based mimicry through score distillation","created":"2023-10-02","cited_by_count":65,"area":"diffusion"},{"id":"2311.13127","title":"MetaCloak: Preventing Unauthorized Subject-driven Text-to-image Diffusion-based Synthesis via Meta-learning","created":"2023-11-22","cited_by_count":44,"area":"diffusion"},{"id":"2312.00084","title":"Can Protective Perturbation Safeguard Personal Data from Being Exploited by Stable Diffusion?","created":"2023-11-30","cited_by_count":38,"area":"diffusion"},{"id":"2306.01902","title":"Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation","created":"2023-06-02","cited_by_count":37,"area":"diffusion"},{"id":"2312.07865","title":"SimAC: A Simple Anti-Customization Method for Protecting Face Privacy against Text-to-Image Synthesis of Diffusion Models","created":"2023-12-13","cited_by_count":36,"area":"diffusion"},{"id":"2408.03400","title":"Attacks and Defenses for Generative Diffusion Models: A Comprehensive Survey","created":"2024-08-06","cited_by_count":32,"area":"diffusion"},{"id":"2311.12066","title":"EditShield: Protecting Unauthorized Image Editing by Instruction-guided Diffusion Models","created":"2023-11-19","cited_by_count":27,"area":"diffusion"},{"id":"2408.00315","title":"ADBM: Adversarial diffusion bridge model for reliable adversarial purification","created":"2024-08-01","cited_by_count":24,"area":"diffusion"},{"id":"2404.09401","title":"Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models","created":"2024-04-15","cited_by_count":23,"area":"diffusion"},{"id":"2411.16832","title":"Edit Away and My Face Will not Stay: Personal Biometric Defense against Malicious Generative Editing","created":"2024-11-25","cited_by_count":21,"area":"diffusion"},{"id":"2308.09889","title":"DUAW: Data-free Universal Adversarial Watermark against Stable Diffusion Customization","created":"2023-08-19","cited_by_count":20,"area":"diffusion"},{"id":"2310.02401","title":"FT-Shield: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models","created":"2023-10-03","cited_by_count":20,"area":"diffusion"},{"id":"2409.05033","title":"A Survey on Diffusion Models for Recommender Systems","created":"2024-09-08","cited_by_count":20,"area":"diffusion"},{"id":"2310.04687","title":"Targeted Attack Improves Protection against Unauthorized Diffusion Customization","created":"2023-10-07","cited_by_count":18,"area":"diffusion"},{"id":"2404.15081","title":"Perturbing Attention Gives You More Bang for the Buck: Subtle Imaging Perturbations That Efficiently Fool Customized Diffusion Models","created":"2024-04-23","cited_by_count":18,"area":"diffusion"},{"id":"2405.19424","title":"Diffusion Policy Attacker: Crafting Adversarial Attacks for Diffusion-based Policies","created":"2024-05-29","cited_by_count":18,"area":"diffusion"},{"id":"2307.16680","title":"On the Trustworthiness Landscape of State-of-the-art Generative Models: A Survey and Outlook","created":"2023-07-31","cited_by_count":15,"area":"diffusion"},{"id":"2408.10571","title":"Prompt-Agnostic Adversarial Perturbation for Customized Diffusion Models","created":"2024-08-20","cited_by_count":14,"area":"diffusion"},{"id":"2408.10901","title":"A Gray-box Attack against Latent Diffusion Model-based Image Editing by Posterior Collapse","created":"2024-08-20","cited_by_count":13,"area":"diffusion"},{"id":"2408.01014","title":"Growth Inhibitors for Suppressing Inappropriate Image Concepts in Diffusion Models","created":"2024-08-02","cited_by_count":12,"area":"diffusion"},{"id":"2408.00001","title":"Replication in Visual Diffusion Models: A Survey and Outlook","created":"2024-07-07","cited_by_count":11,"area":"diffusion"},{"id":"2503.10081","title":"AdvPaint: Protecting Images from Inpainting Manipulation via Adversarial Attention Disruption","created":"2025-03-13","cited_by_count":11,"area":"diffusion"},{"id":"2404.13320","title":"Pixel is a Barrier: Diffusion Models Are More Adversarially Robust Than We Think","created":"2024-04-20","cited_by_count":10,"area":"diffusion"},{"id":"2403.19254","title":"Imperceptible Protection against Style Imitation from Diffusion Models","created":"2024-03-28","cited_by_count":9,"area":"diffusion"},{"id":"2505.18766","title":"StyleGuard: Preventing Text-to-Image-Model-based Style Mimicry Attacks by Style Perturbations","created":"2025-05-24","cited_by_count":8,"area":"diffusion"},{"id":"2506.01591","title":"Silence is Golden: Leveraging Adversarial Examples to Nullify Audio Control in LDM-based Talking-Head Generation","created":"2025-06-02","cited_by_count":8,"area":"diffusion"},{"id":"2412.11423","title":"Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models","created":"2024-12-16","cited_by_count":7,"area":"diffusion"},{"id":"2504.17894","title":"DCT-Shield: A Robust Frequency Domain Defense against Malicious Image Editing","created":"2025-04-24","cited_by_count":7,"area":"diffusion"},{"id":"2512.16625","title":"DeContext as Defense: Safe Image Editing in Diffusion Transformers","created":"2025-12-18","cited_by_count":7,"area":"diffusion"},{"id":"2603.24570","title":"Anti-I2V: Safeguarding your photos from malicious image-to-video generation","created":"2026-03-25","cited_by_count":7,"area":"diffusion"},{"id":"2403.11162","title":"CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion","created":"2024-03-17","cited_by_count":6,"area":"diffusion"},{"id":"2406.15305","title":"PID: Prompt-Independent Data Protection Against Latent Diffusion Models","created":"2024-06-14","cited_by_count":6,"area":"diffusion"},{"id":"2409.12314","title":"Understanding Implosion in Text-to-Image Generative Models","created":"2024-09-18","cited_by_count":6,"area":"diffusion"},{"id":"2311.13713","title":"A Somewhat Robust Image Watermark against Diffusion-based Editing Models","created":"2023-11-22","cited_by_count":5,"area":"diffusion"},{"id":"2401.07087","title":"Exploring Adversarial Attacks against Latent Diffusion Model from the Perspective of Adversarial Transferability","created":"2024-01-13","cited_by_count":5,"area":"diffusion"},{"id":"2412.09844","title":"Real-time Identity Defenses against Malicious Personalization of Diffusion Models","created":"2024-12-13","cited_by_count":5,"area":"diffusion"},{"id":"2503.13576","title":"A Comprehensive Survey on Visual Concept Mining in Text-to-image Diffusion Models","created":"2025-03-17","cited_by_count":5,"area":"diffusion"},{"id":"2505.15336","title":"My Face Is Mine, Not Yours: Facial Protection Against Diffusion Model Face Swapping","created":"2025-05-21","cited_by_count":5,"area":"diffusion"},{"id":"2512.23953","title":"T2VAttack: Adversarial Attack on Text-to-Video Diffusion Models","created":"2025-12-30","cited_by_count":5,"area":"diffusion"},{"id":"2406.18944","title":"Rethinking and Red-Teaming Protective Perturbation in Personalized Diffusion Models","created":"2024-06-27","cited_by_count":4,"area":"diffusion"},{"id":"2502.07225","title":"CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models","created":"2025-02-11","cited_by_count":4,"area":"diffusion"},{"id":"2503.11071","title":"Harnessing Frequency Spectrum Insights for Image Copyright Protection Against Diffusion Models","created":"2025-03-14","cited_by_count":4,"area":"diffusion"},{"id":"2505.08197","title":"Visual Watermarking in the Era of Diffusion Models: Advances and Challenges","created":"2025-05-13","cited_by_count":4,"area":"diffusion"},{"id":"2509.13711","title":"StyleProtect: Safeguarding Artistic Identity in Fine-tuned Diffusion Models","created":"2025-09-17","cited_by_count":4,"area":"diffusion"},{"id":"2306.00080","title":"AI Imagery and the Overton Window","created":"2023-05-31","cited_by_count":3,"area":"diffusion"},{"id":"2408.11810","title":"Pixel Is Not a Barrier: An Effective Evasion Attack for Pixel-Domain Diffusion Models","created":"2024-08-21","cited_by_count":3,"area":"diffusion"},{"id":"2411.11688","title":"Watermarking Visual Concepts for Diffusion Models","created":"2024-11-18","cited_by_count":3,"area":"diffusion"},{"id":"2507.03953","title":"Evaluating Adversarial Protections for Diffusion Personalization: A Comprehensive Study","created":"2025-07-05","cited_by_count":3,"area":"diffusion"},{"id":"2509.13922","title":"Towards Robust Defense against Customization via Protective Perturbation Resistant to Diffusion-based Purification","created":"2025-09-17","cited_by_count":3,"area":"diffusion"},{"id":"2511.01307","title":"Perturb a Model, Not an Image: Towards Robust Privacy Protection via Anti-Personalized Diffusion Models","created":"2025-11-03","cited_by_count":3,"area":"diffusion"},{"id":"2409.18897","title":"Detecting Dataset Abuse in Fine-Tuning Stable Diffusion Models for Text-to-Image Synthesis","created":"2024-09-27","cited_by_count":2,"area":"diffusion"},{"id":"2411.16437","title":"Privacy Protection in Personalized Diffusion Models via Targeted Cross-Attention Adversarial Attack","created":"2024-11-25","cited_by_count":2,"area":"diffusion"},{"id":"2412.18791","title":"Protective Perturbations against Unauthorized Data Usage in Diffusion-based Image Generation","created":"2024-12-25","cited_by_count":2,"area":"diffusion"},{"id":"2504.12129","title":"Anti-Aesthetics: Protecting Facial Privacy against Customized Text-to-Image Synthesis","created":"2025-04-16","cited_by_count":2,"area":"diffusion"},{"id":"2504.12747","title":"Privacy Protection Against Personalized Text-to-Image Synthesis via Cross-image Consistency Constraints","created":"2025-04-17","cited_by_count":2,"area":"diffusion"},{"id":"2507.10578","title":"When and Where do Data Poisons Attack Textual Inversion?","created":"2025-07-11","cited_by_count":2,"area":"diffusion"},{"id":"2507.17554","title":"An h-space Based Adversarial Attack for Protection Against Few-shot Personalization","created":"2025-07-23","cited_by_count":2,"area":"diffusion"},{"id":"2508.16217","title":"PromptFlare: Prompt-Generalized Defense via Cross-Attention Decoy in Diffusion-Based Inpainting","created":"2025-08-22","cited_by_count":2,"area":"diffusion"},{"id":"2510.00778","title":"DIA: The Adversarial Exposure of Deterministic Inversion in Diffusion Models","created":"2025-10-01","cited_by_count":2,"area":"diffusion"},{"id":"2603.13028","title":"Purify Once, Edit Freely: Breaking Image Protections under Model Mismatch","created":"2026-03-13","cited_by_count":2,"area":"diffusion"},{"id":"2606.09909","title":"Bypassing Copyright Protection in Diffusion-based Customization via Two-Stage Latent Feature Optimization","created":"2026-06-06","cited_by_count":2,"area":"diffusion"},{"id":"2408.11408","title":"Latent Feature and Attention Dual Erasure Attack against Multi-View Diffusion Models for 3D Assets Protection","created":"2024-08-21","cited_by_count":1,"area":"diffusion"},{"id":"2410.03039","title":"Leveraging Model Guidance to Extract Training Data from Personalized Diffusion Models","created":"2024-10-03","cited_by_count":1,"area":"diffusion"},{"id":"2410.03640","title":"Real-World Benchmarks Make Membership Inference Attacks Fail on Diffusion Models","created":"2024-10-04","cited_by_count":1,"area":"diffusion"},{"id":"2502.16167","title":"PersGuard: Preventing Malicious Personalization in Text-to-Image Diffusion Models via Model Backdoors","created":"2025-02-22","cited_by_count":1,"area":"diffusion"},{"id":"2505.19425","title":"Structure Disruption: Subverting Malicious Diffusion-Based Inpainting via Self-Attention Query Perturbation","created":"2025-05-26","cited_by_count":1,"area":"diffusion"},{"id":"2506.04394","title":"Is Perturbation-Based Image Protection Disruptive to Image Editing?","created":"2025-06-04","cited_by_count":1,"area":"diffusion"},{"id":"2510.03089","title":"Latent Diffusion Unlearning: Protecting Against Unauthorized Personalization Through Trajectory Shifted Perturbations","created":"2025-10-03","cited_by_count":1,"area":"diffusion"},{"id":"2512.10433","title":"Targeted Data Protection for Diffusion Model by Matching Training Trajectory","created":"2025-12-11","cited_by_count":1,"area":"diffusion"},{"id":"2601.14738","title":"Safeguarding Facial Identity against Diffusion-based Face Swapping via Cascading Pathway Disruption","created":"2026-01-21","cited_by_count":1,"area":"diffusion"},{"id":"2604.00903","title":"IDDM: Identity-Decoupled Personalized Diffusion Models with a Tunable Privacy-Utility Trade-off","created":"2026-04-01","cited_by_count":1,"area":"diffusion"},{"id":"2410.05694","title":"DiffusionGuard: A Robust Defense Against Malicious Diffusion-based Image Editing","created":"2024-10-08","cited_by_count":0,"area":"diffusion"},{"id":"2505.21742","title":"What is Adversarial Training for Diffusion Models?","created":"2025-05-27","cited_by_count":0,"area":"diffusion"},{"id":"2510.26105","title":"Security Risk of Misalignment between Text and Image in Multi-modal Model","created":"2025-10-30","cited_by_count":0,"area":"diffusion"},{"id":"2601.07253","title":"Universal Adversarial Purification with DDIM Metric Loss for Stable Diffusion","created":"2026-01-12","cited_by_count":0,"area":"diffusion"},{"id":"2602.10319","title":"A Low-Rank Defense Method for Adversarial Attack on Diffusion Models","created":"2026-02-10","cited_by_count":0,"area":"diffusion"},{"id":"2606.12263","title":"VOID: Defeating Unauthorized Mimicry in Latent Diffusion Models","created":"2026-06-10","cited_by_count":0,"area":"diffusion"},{"id":"2607.02038","title":"Hierarchical Anti-Aesthetics: Protecting Facial Privacy against Customized Diffusion Models","created":"2026-07-02","cited_by_count":0,"area":"diffusion"},{"id":"2609.05503","title":"A Survey on Adversarial Attacks and Defenses for Diffusion Models Across Multiple Modalities","created":"2026-08-28","cited_by_count":0,"area":"diffusion"},{"id":"2609.15799","title":"RAPID: A Real-Time Defense Against Unauthorized Model Distillation for Text-to-Image Services","created":"2026-09-14","cited_by_count":0,"area":"diffusion"},{"id":"2609.30980","title":"FeatMark: Feature-level Watermark Protection against Mimicry Attacks with Diffusion Models","created":"2026-09-25","cited_by_count":0,"area":"diffusion"},{"id":"2610.13333","title":"LETHE: Defending against Unauthorized Image-to-Video Generation via Value Diversity Collapse","created":"2026-10-09","cited_by_count":0,"area":"diffusion"},{"id":"2610.16835","title":"Beyond User-Side Defenses: Image Protection in Open-Weight Diffusion Models","created":"2026-10-12","cited_by_count":0,"area":"diffusion"},{"id":"2610.19717","title":"No Way To Steal My Face: Proactive Defense Against Identity-Preserving Personalized Generation","created":"2026-10-14","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}