Compute Resources for Style Mimicry Experiments

Written by torts | Published 2024/12/15
Tech Story Tags: ai-forgery | generative-ai | ai-style-mimicry | image-theft-by-ai | protecting-art-from-ai | glaze-protection-tool | black-box-ai-access | user-study-on-ai-art-mimicry

TLDRThis section outlines the compute resources used in our experiments, including execution times for generating images and the additional time required by IMPRESS++.via the TL;DR App

Table of Links

Abstract and 1. Introduction

  1. Background and Related Work

  2. Threat Model

  3. Robust Style Mimicry

  4. Experimental Setup

  5. Results

    6.1 Main Findings: All Protections are Easily Circumvented

    6.2 Analysis

  6. 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

L Compute Resources

Table 4 reports the compute resources for our experiments.

Authors:

(1) Robert Honig, ETH Zurich ([email protected]);

(2) Javier Rando, ETH Zurich ([email protected]);

(3) Nicholas Carlini, Google DeepMind;

(4) Florian Tramer, ETH Zurich ([email protected]).

This paper is available on arxiv under CC BY 4.0 license.

Written by torts | Exploring the legal landscape of the digital age. Read my articles to understand the why behind the what.
Published by HackerNoon on 2024/12/15
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