mlsanityData to 14 Oct 2026

Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI

arXiv 2406.12027 ↗First posted 17 Jun 2024cs.CRcited by 38

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Abstract (arXiv)

Artists are increasingly concerned about advancements in image generation models that can closely replicate their unique artistic styles. In response, several protection tools against style mimicry have been developed that incorporate small adversarial perturbations into artworks published online. In this work, we evaluate the effectiveness of popular protections -- with millions of downloads -- and show they only provide a false sense of security. We find that low-effort and "off-the-shelf" techniques, such as image upscaling, are sufficient to create robust mimicry methods that significantly degrade existing protections. Through a user study, we demonstrate that all existing protections can be easily bypassed, leaving artists vulnerable to style mimicry. We caution that tools based on adversarial perturbations cannot reliably protect artists from the misuse of generative AI, and urge the development of alternative non-technological solutions.

Key facts. Cited by 38 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Jun 2024.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 9; 2025: 16; 2026: 13.

38 papers.

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  1. 2024An Adversarial Perspective on Machine Unlearning for AI Safetycited by 123
  2. 2024On Evaluating the Durability of Safeguards for Open-Weight LLMscited by 52
  3. 2024Adversarial Attacks of Vision Tasks in the Past 10 Years: A Surveycited by 23
  4. 2025Position: Adversarial ML for LLMs Is Not Making Any Progresscited by 15
  5. 2024Dormant: Defending against Pose-driven Human Image Animationcited by 11
  6. 2025AdvPaint: Protecting Images from Inpainting Manipulation via Adversarial Attention Disruptioncited by 11
  7. 2024AdvI2I: Adversarial Image Attack on Image-to-Image Diffusion modelscited by 10
  8. 2025Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectivescited by 8
  9. 2025StyleGuard: Preventing Text-to-Image-Model-based Style Mimicry Attacks by Style Perturbationscited by 8
  10. 2025Vid-Freeze: Protecting Images from Malicious Image-to-Video Generation via Temporal Freezingcited by 8
  11. 2024Copyright-Protected Language Generation via Adaptive Model Fusioncited by 7
  12. 2024A LoRA is Worth a Thousand Picturescited by 7
26 more
  1. 2026Universal Image Immunization against Diffusion-based Image Editing via Semantic Injectioncited by 7
  2. 2025Enhancing Variational Autoencoders with Smooth Robust Latent Encodingcited by 6
  3. 2025GuardDoor: Safeguarding Against Malicious Diffusion Editing via Protective Backdoorscited by 5
  4. 2025BlurGuard: A Simple Approach for Robustifying Image Protection Against AI-Powered Editingcited by 5
  5. 2025CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Modelscited by 4
  6. 2024BridgePure: Limited Protection Leakage Can Break Black-Box Data Protectioncited by 3
  7. 2025Perturb a Model, Not an Image: Towards Robust Privacy Protection via Anti-Personalized Diffusion Modelscited by 3
  8. 2025An h-space Based Adversarial Attack for Protection Against Few-shot Personalizationcited by 2
  9. 2025Phi: Preference Hijacking in Multi-modal Large Language Models at Inference Timecited by 2
  10. 2025DIA: The Adversarial Exposure of Deterministic Inversion in Diffusion Modelscited by 2
  11. 2026Purify Once, Edit Freely: Breaking Image Protections under Model Mismatchcited by 2
  12. 2025Is Perturbation-Based Image Protection Disruptive to Image Editing?cited by 1
  13. 2025Anti-Tamper Protection for Unauthorized Individual Image Generationcited by 1
  14. 2026Off-The-Shelf Image-to-Image Models Are All You Need To Defeat Image Protection Schemescited by 1
  15. 2026What Does It Mean to Break a Distillation Defense?cited by 1
  16. 2024GLEAN: Generative Learning for Eliminating Adversarial Noisecited by 0
  17. 2025AegisRF: Adversarial Perturbations Guided with Sensitivity for Protecting Intellectual Property of Neural Radiance Fieldscited by 0
  18. 2026Identifying Models Behind Text-to-Image Leaderboardscited by 0
  19. 2026SoK: Unlearnability and Unlearning for Model Dememorizationcited by 0
  20. 2026VOID: Defeating Unauthorized Mimicry in Latent Diffusion Modelscited by 0
  21. 2026Natural Identifiers for Privacy and Data Audits in Large Language Modelscited by 0
  22. 2026Position: Preventing AI-Generated CSAM Necessitates New Approaches to AI Safetycited by 0
  23. 2026Position: AI/ML Deepfake Research is Misaligned with AI-Generated Non-Consensual Intimate Imagery (AIG-NCII)cited by 0
  24. 2026A Survey on Adversarial Attacks and Defenses for Diffusion Models Across Multiple Modalitiescited by 0
  25. 2026FeatMark: Feature-level Watermark Protection against Mimicry Attacks with Diffusion Modelscited by 0
  26. 2026Preemptive LLM Unlearning against Forbidden Capability Acquisition via Gradient Sealingcited by 0