Table of Links Abstract and 1. Introduction Background and Related Work


Threat Model


Robust Style Mimicry


Experimental Setup


Results
6.1 Main Findings: All Protections are Easily Circumvented
6.2 Analysis


Discussion and Broader Impact, Acknowledgements, and References A. Detailed Art Examples B. Robust Mimicry Generations C. Detailed Results D. Differences with Glaze Finetuning E. Findings on Glaze 2.0 F. Findings on Mist v2 G. Methods for Style Mimicry H. Existing Style Mimicry Protections I. Robust Mimicry Methods J. Experimental Setup K. User Study L. Compute Resources 2 Background and Related Work Text-to-image diffusion models. A latent diffusion model consists of an image autoencoder and a denoiser. The autoencoder is trained to encode and decode images using a lower-dimensional latent space. The denoiser predicts the noise added to latent representations of images in a diffusion process (Ho et al., 2020). Latent diffusion models can generate images from text prompts by conditioning the denoiser on image captions (Rombach et al., 2022). Popular text-to-image diffusion models include open models such as Stable Diffusion (Rombach et al., 2022) and Kandinsky (Razzhigaev et al., 2023), as well as closed models like Imagen (Saharia et al., 2022) and DALL-E (Ramesh et al.; Betker et al., 2023). Style mimicry. Style mimicry uses generative models to create images matching a target artistic style. Existing techniques vary in complexity and quality (see Appendix G). An effective method is to finetune a diffusion model using a few images in the targeted style. Some artists worry that style mimicry can be misused to reproduce their work without permission and steal away customers (Heikkila¨, 2022). Style mimicry protections. Several tools have been proposed to prevent unauthorized style mimicry. These tools allow artists to include small perturbations—optimized to disrupt style mimicry techniques—in their images before publishing. The most popular protections are Glaze (Shan et al., 2023a) and Mist (Liang et al., 2023). Additionally, Anti-DreamBooth (Van Le et al., 2023) was introduced to prevent fake personalized images, but we also find it effective for style mimicry. Both Glaze and Mist target the encoder in latent diffusion models; they perturb images to obtain latent representations that decode to images in a different style (see Appendix H.1). On the other hand, Anti-DreamBooth targets the denoiser and maximizes the prediction error on the latent representations of the perturbed images (see Appendix H.2). Circumventing style mimicry protections. Although not initially designed for this purpose, adversarial purification (Yoon et al., 2021; Shi et al., 2020; Samangouei et al., 2018) could be used to remove the perturbations introduced by style mimicry protections. DiffPure (Nie et al., 2022) is the strongest purification method and Mist claims robustness against it. Another existing method for purification is upscaling (Mustafa et al., 2019). Similarly, Mist and Glaze claim robustness against upscaling. Section 4.1 highlights flaws in previous evaluations and how a careful application of both methods can effectively remove mimicry protections. IMPRESS (Cao et al., 2024) was the first purification method designed specifically to circumvent style mimicry protections. While IMPRESS claims to circumvent Glaze, the authors of Glaze critique the method’s evaluation (Shan et al., 2023b), namely the reliance on automated metrics instead of a user study, as well as the method’s poor performance on contemporary artists. Our work addresses these limitations by considering simpler and stronger purification methods, and evaluating them rigorously with a user study and across a variety of historical and contemporary artists. Our results show that the main idea of IMPRESS is sound, and that very similar robust mimicry methods are effective. Unlearnable examples . Style mimicry protections build upon a line of work that aims to make data “unlearnable” by machine learning models (Shan et al., 2020; Huang et al., 2021; Cherepanova et al., 2021; Salman et al., 2023). These methods typically rely on some form of adversarial optimization, inspired by adversarial examples (Szegedy et al., 2013). Ultimately, these techniques always fall short of an adaptive adversary that enjoys a second-mover advantage: once unlearnable examples have been collected, their protection can no longer be changed, and the adversary can thereafter select a learning method tailored towards breaking the protections (Radiya-Dixit et al., 2021; Fowl et al., 2021; Tao et al., 2021). Authors:
(1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch);
(2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch);
(3) Nicholas Carlini, Google DeepMind;
(4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). This paper is available on arxiv under CC BY 4.0 license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background and Related Work Threat Model Robust Style Mimicry Experimental Setup Results
6.1 Main Findings: All Protections are Easily Circumvented
6.2 Analysis Discussion and Broader Impact, Acknowledgements, and References Background and Related Work Background and Related Work Background and Related Work Threat Model Threat Model Threat Model Robust Style Mimicry Robust Style Mimicry Robust Style Mimicry Experimental Setup Experimental Setup Experimental Setup Results 6.1 Main Findings: All Protections are Easily Circumvented 6.2 Analysis Results Results 6.1 Main Findings: All Protections are Easily Circumvented 6.1 Main Findings: All Protections are Easily Circumvented 6.2 Analysis 6.2 Analysis Discussion and Broader Impact, Acknowledgements, and References Discussion and Broader Impact, Acknowledgements, and References Discussion and Broader Impact, Acknowledgements, and References A. Detailed Art Examples A. Detailed Art Examples B. Robust Mimicry Generations B. Robust Mimicry Generations C. Detailed Results C. Detailed Results D. Differences with Glaze Finetuning D. Differences with Glaze Finetuning E. Findings on Glaze 2.0 E. Findings on Glaze 2.0 F. Findings on Mist v2 F. Findings on Mist v2 G. Methods for Style Mimicry G. Methods for Style Mimicry H. Existing Style Mimicry Protections H. Existing Style Mimicry Protections I. Robust Mimicry Methods I. Robust Mimicry Methods J. Experimental Setup J. Experimental Setup K. User Study K. User Study L. Compute Resources L. Compute Resources 2 Background and Related Work Text-to-image diffusion models . A latent diffusion model consists of an image autoencoder and a denoiser. The autoencoder is trained to encode and decode images using a lower-dimensional latent space. The denoiser predicts the noise added to latent representations of images in a diffusion process (Ho et al., 2020). Latent diffusion models can generate images from text prompts by conditioning the denoiser on image captions (Rombach et al., 2022). Popular text-to-image diffusion models include open models such as Stable Diffusion (Rombach et al., 2022) and Kandinsky (Razzhigaev et al., 2023), as well as closed models like Imagen (Saharia et al., 2022) and DALL-E (Ramesh et al.; Betker et al., 2023). Text-to-image diffusion models Style mimicry. Style mimicry uses generative models to create images matching a target artistic style. Existing techniques vary in complexity and quality (see Appendix G). An effective method is to finetune a diffusion model using a few images in the targeted style. Some artists worry that style mimicry can be misused to reproduce their work without permission and steal away customers (Heikkila¨, 2022). Style mimicry. Style mimicry protections. Several tools have been proposed to prevent unauthorized style mimicry. These tools allow artists to include small perturbations—optimized to disrupt style mimicry techniques—in their images before publishing. The most popular protections are Glaze (Shan et al., 2023a) and Mist (Liang et al., 2023). Additionally, Anti-DreamBooth (Van Le et al., 2023) was introduced to prevent fake personalized images, but we also find it effective for style mimicry. Both Glaze and Mist target the encoder in latent diffusion models; they perturb images to obtain latent representations that decode to images in a different style (see Appendix H.1). On the other hand, Anti-DreamBooth targets the denoiser and maximizes the prediction error on the latent representations of the perturbed images (see Appendix H.2). Style mimicry protections. Circumventing style mimicry protections. Although not initially designed for this purpose, adversarial purification (Yoon et al., 2021; Shi et al., 2020; Samangouei et al., 2018) could be used to remove the perturbations introduced by style mimicry protections. DiffPure (Nie et al., 2022) is the strongest purification method and Mist claims robustness against it. Another existing method for purification is upscaling (Mustafa et al., 2019). Similarly, Mist and Glaze claim robustness against upscaling. Section 4.1 highlights flaws in previous evaluations and how a careful application of both methods can effectively remove mimicry protections. Circumventing style mimicry protections. IMPRESS (Cao et al., 2024) was the first purification method designed specifically to circumvent style mimicry protections. While IMPRESS claims to circumvent Glaze, the authors of Glaze critique the method’s evaluation (Shan et al., 2023b), namely the reliance on automated metrics instead of a user study, as well as the method’s poor performance on contemporary artists. Our work addresses these limitations by considering simpler and stronger purification methods, and evaluating them rigorously with a user study and across a variety of historical and contemporary artists. Our results show that the main idea of IMPRESS is sound, and that very similar robust mimicry methods are effective. Unlearnable examples . Style mimicry protections build upon a line of work that aims to make data “unlearnable” by machine learning models (Shan et al., 2020; Huang et al., 2021; Cherepanova et al., 2021; Salman et al., 2023). These methods typically rely on some form of adversarial optimization, inspired by adversarial examples (Szegedy et al., 2013). Ultimately, these techniques always fall short of an adaptive adversary that enjoys a second-mover advantage: once unlearnable examples have been collected, their protection can no longer be changed, and the adversary can thereafter select a learning method tailored towards breaking the protections (Radiya-Dixit et al., 2021; Fowl et al., 2021; Tao et al., 2021). Unlearnable examples Authors: (1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch); (2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch); (3) Nicholas Carlini, Google DeepMind; (4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). Authors: Authors: (1) Robert Honig, ETH Zurich (robert.hoenig@inf.ethz.ch); (2) Javier Rando, ETH Zurich (javier.rando@inf.ethz.ch); (3) Nicholas Carlini, Google DeepMind; (4) Florian Tramer, ETH Zurich (florian.tramer@inf.ethz.ch). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv

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