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 C Detailed Results C.1 Mimicry Quality Versus Style This section includes the detailed results from our user study. As mentioned in Section 5, we ask users to assess quality and stylistic fit separately in our study. Figure 16 and 17 show the results for each of these evaluations separately (the results in the main body represent the average of the two). Finally, Table 1 includes numerical results for each scenario. C.2 Results Broken Down per Artist We present next the results obtained for each artist in each scenario. Table 2 plots the success rate for each method against each protection for all artists, and Table 3 includes the detailed success rates. Table 3: User preference ratings of all style mimicry scenarios S ∈ M for each artist A ∈ A by name. Each cell states the percentage of votes that prefer an image generated under the corresponding scenario S and artist A ∈ A over a matching image generated under clean style mimicry. Higher percentages indicate weaker attacks or better defenses. C.3 Inter-Annotator Agreement 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 C Detailed Results C.1 Mimicry Quality Versus Style This section includes the detailed results from our user study. As mentioned in Section 5, we ask users to assess quality and stylistic fit separately in our study. Figure 16 and 17 show the results for each of these evaluations separately (the results in the main body represent the average of the two). Finally, Table 1 includes numerical results for each scenario. C.2 Results Broken Down per Artist We present next the results obtained for each artist in each scenario. Table 2 plots the success rate for each method against each protection for all artists, and Table 3 includes the detailed success rates. Table 3: User preference ratings of all style mimicry scenarios S ∈ M for each artist A ∈ A by name. Each cell states the percentage of votes that prefer an image generated under the corresponding scenario S and artist A ∈ A over a matching image generated under clean style mimicry. Higher percentages indicate weaker attacks or better defenses. C.3 Inter-Annotator Agreement 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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