# Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI

arXiv: https://arxiv.org/abs/2406.12027 · HTML: https://mlsanity.com/paper/2406.12027/

## Key facts

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

## Built on by (most cited first, 30 of 38)

- [An Adversarial Perspective on Machine Unlearning for AI Safety](https://mlsanity.com/paper/2409.18025/) (2024)
- [On Evaluating the Durability of Safeguards for Open-Weight LLMs](https://mlsanity.com/paper/2412.07097/) (2024)
- [Adversarial Attacks of Vision Tasks in the Past 10 Years: A Survey](https://mlsanity.com/paper/2410.23687/) (2024)
- [Position: Adversarial ML for LLMs Is Not Making Any Progress](https://mlsanity.com/paper/2502.02260/) (2025)
- [Dormant: Defending against Pose-driven Human Image Animation](https://mlsanity.com/paper/2409.14424/) (2024)
- [AdvPaint: Protecting Images from Inpainting Manipulation via Adversarial Attention Disruption](https://mlsanity.com/paper/2503.10081/) (2025)
- [AdvI2I: Adversarial Image Attack on Image-to-Image Diffusion models](https://mlsanity.com/paper/2410.21471/) (2024)
- [Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives](https://mlsanity.com/paper/2502.11910/) (2025)
- [StyleGuard: Preventing Text-to-Image-Model-based Style Mimicry Attacks by Style Perturbations](https://mlsanity.com/paper/2505.18766/) (2025)
- [Vid-Freeze: Protecting Images from Malicious Image-to-Video Generation via Temporal Freezing](https://mlsanity.com/paper/2509.23279/) (2025)
- [Copyright-Protected Language Generation via Adaptive Model Fusion](https://mlsanity.com/paper/2412.06619/) (2024)
- [A LoRA is Worth a Thousand Pictures](https://mlsanity.com/paper/2412.12048/) (2024)
- [Universal Image Immunization against Diffusion-based Image Editing via Semantic Injection](https://mlsanity.com/paper/2602.14679/) (2026)
- [Enhancing Variational Autoencoders with Smooth Robust Latent Encoding](https://mlsanity.com/paper/2504.17219/) (2025)
- [GuardDoor: Safeguarding Against Malicious Diffusion Editing via Protective Backdoors](https://mlsanity.com/paper/2503.03944/) (2025)
- [BlurGuard: A Simple Approach for Robustifying Image Protection Against AI-Powered Editing](https://mlsanity.com/paper/2511.00143/) (2025)
- [CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models](https://mlsanity.com/paper/2502.07225/) (2025)
- [BridgePure: Limited Protection Leakage Can Break Black-Box Data Protection](https://mlsanity.com/paper/2412.21061/) (2024)
- [Perturb a Model, Not an Image: Towards Robust Privacy Protection via Anti-Personalized Diffusion Models](https://mlsanity.com/paper/2511.01307/) (2025)
- [An h-space Based Adversarial Attack for Protection Against Few-shot Personalization](https://mlsanity.com/paper/2507.17554/) (2025)
- [Phi: Preference Hijacking in Multi-modal Large Language Models at Inference Time](https://mlsanity.com/paper/2509.12521/) (2025)
- [DIA: The Adversarial Exposure of Deterministic Inversion in Diffusion Models](https://mlsanity.com/paper/2510.00778/) (2025)
- [Purify Once, Edit Freely: Breaking Image Protections under Model Mismatch](https://mlsanity.com/paper/2603.13028/) (2026)
- [Is Perturbation-Based Image Protection Disruptive to Image Editing?](https://mlsanity.com/paper/2506.04394/) (2025)
- [Anti-Tamper Protection for Unauthorized Individual Image Generation](https://mlsanity.com/paper/2508.06325/) (2025)
- [Off-The-Shelf Image-to-Image Models Are All You Need To Defeat Image Protection Schemes](https://mlsanity.com/paper/2602.22197/) (2026)
- [What Does It Mean to Break a Distillation Defense?](https://mlsanity.com/paper/2606.25059/) (2026)
- [GLEAN: Generative Learning for Eliminating Adversarial Noise](https://mlsanity.com/paper/2409.10578/) (2024)
- [AegisRF: Adversarial Perturbations Guided with Sensitivity for Protecting Intellectual Property of Neural Radiance Fields](https://mlsanity.com/paper/2510.19371/) (2025)
- [Identifying Models Behind Text-to-Image Leaderboards](https://mlsanity.com/paper/2601.09647/) (2026)

## 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.