![]() App out nowġ/ This might be the most important oil painting I’ve made: The first painting released to the world that utilizes Glaze, a protective tech against unethical AI/ML models, developed by the team led by. “1/ This might be the most important oil painting I’ve made: The three artists- Sarah Andersen, Kelly McKernan, and Karla Ortiz, are alleging that these companies have infringed on the rights of “millions of artists” by training their AI on the data of five billion images scraped from the web, “without the consent of the original artists.” The release of Glaze was done in collaboration with Puerto Rican artist Karla Ortiz, who created the oil painting ‘Musa Victoriosa’ to release alongside the program, as the first artwork to use its “cloak.” Ortiz is part of a trio of artists currently involved in a lawsuit in which they are suing Stability AI (Stable Diffusion) and Midjourney (Midjourney). ![]() Until then, cloaking works precisely because of fundamental weaknesses in how AI models are designed today.” Glaze’s release When asked if AI models may eventually evolve past the point of programs like Glaze’s security, the website said, “It’s certainly possible, but we expect that would require significant changes in the underlying architecture of AI models. Zhao’s response to this was that the project is still in early development, meaning updates will be made, “We still need to fix some basic things and then will look at better ways to minimize visual impact.” Adversarial examples have been recognized since 2014 ( here‘s one of the first papers on the topic), and numerous papers have attempted to prevent these adversarial examples since.” User concerns with GlazeĬurrently, one of the main topics of concern from users is that their art sometimes picks up additional artifacts when being compressed, dependent on the style of their art. The Glaze Project – University of Chicago website explains the topic further, “the Achilles’ heel for AI models has been a phenomenon called adversarial examples– small tweaks in inputs that can produce massive differences in how AI models classify the input. Glaze performs this feat by using a concept called adversarial examples.
0 Comments
Leave a Reply. |