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 F Findings on Mist v2 After responsibly disclosing our work to defense developers, authors from Mist brought to our attention the recent release of their latest Mist v2 with improved resilience (Zheng et al., 2023). As we did with Glaze v2.0 (see Section E), we reproduced some of our experiments with the latest protections to verify the success of robust mimicry. Their original implementation still uses the outdated version 1.5 of Stable Diffusion. We change to SD 2.1 to match our previous experiments[6]. Our findings, as we saw with Glaze v2.0, highlight that improved protections are still not effective against low-effort robust mimicry. More specifically, the latest version of Mist: introduces visible perturbations over the images. See Figure 23


does not improve protections against robust mimicry. See Figure 24


creates protection that are easily removable with Noisy Upscaling. See Figure 25. 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. [6] Both models share the same encoder for which protections are optimized. 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 F Findings on Mist v2 After responsibly disclosing our work to defense developers, authors from Mist brought to our attention the recent release of their latest Mist v2 with improved resilience (Zheng et al., 2023). As we did with Glaze v2.0 (see Section E), we reproduced some of our experiments with the latest protections to verify the success of robust mimicry. Their original implementation still uses the outdated version 1.5 of Stable Diffusion. We change to SD 2.1 to match our previous experiments[6]. Our findings, as we saw with Glaze v2.0, highlight that improved protections are still not effective against low-effort robust mimicry. More specifically, the latest version of Mist: introduces visible perturbations over the images. See Figure 23 does not improve protections against robust mimicry. See Figure 24 creates protection that are easily removable with Noisy Upscaling. See Figure 25. introduces visible perturbations over the images. See Figure 23 introduces visible perturbations over the images. See Figure 23 does not improve protections against robust mimicry. See Figure 24 does not improve protections against robust mimicry. See Figure 24 creates protection that are easily removable with Noisy Upscaling. See Figure 25. creates protection that are easily removable with Noisy Upscaling. See Figure 25. 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 [6] Both models share the same encoder for which protections are optimized.

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