Evaluating Glaze's Effectiveness: A Critical Analysis of AI Art Protection Through Non-Artist Perspectives and Common Image Transformations

Abstract

This paper presents a systematic evaluation of Glaze 2.1, a tool designed to protect artists' styles from AI mimicry. We examine its effectiveness against common image transformations typically applied by social media platforms and assess its protection capabilities through the perspective of non-artist users. Our methodology combines technical analysis of how transformations like JPEG compression, scaling, blurring, and sharpening affect Glaze's protective perturbations with a comprehensive user study involving participants without specific artistic expertise. Results indicate that Glaze exhibits significant vulnerabilities when protected images undergo standard social media processing, with certain transformations substantially reducing its effectiveness. These findings highlight the challenges in developing robust protection mechanisms that can withstand real-world usage scenarios while remaining practical for artists. We contribute valuable insights into the limitations of current AI art protection tools and suggest directions for developing more resilient solutions that can better safeguard artists' intellectual property in digital environments.

Keywords: Adversarial Machine Learning, AI Art Protection, Style Mimicry, Image Transformations, Glaze, Social Media Processing

DOI: 10.54941/ahfe1006086