The emergence of text-to-image generator models has transformed the art industry, allowing anyone to create detailed artwork by providing text prompts. These AI models have gained recognition, won awards, and found applications in various media. However, their widespread use has negatively impacted independent artists, displacing their work and undermining their ability to make a living.
Glaze has been developed to address the issue of style mimicry. Glaze enables artists to protect their unique styles by applying minimal perturbations, known as “style cloaks,” to their artwork. These perturbations shift the representation of the artwork in the generator model’s feature space, teaching the model to associate the artist with a different style. As a result, when AI models attempt to mimic the artist’s style, they generate artwork that does not match the artist’s authentic style.
Glaze was developed in collaboration with professional artists and underwent rigorous evaluation through user studies. The majority of surveyed artists found the perturbations to be minimal and not disruptive to the value of their art. The system effectively disrupted style mimicry by AI models, even when tested against real-world mimicry platforms. Importantly, Glaze remained effective in scenarios where artists had already posted significant amounts of artwork online.
Glaze provides a technical solution to protect artists from style mimicry in the AI-dominated art landscape. Glaze offers an effective defense mechanism by engaging with professional artists and understanding their concerns. Glaze empowers artists to safeguard their artistic styles and maintain their creative integrity by applying minimal perturbations.
The system’s implementation involved computing carefully designed style cloaks, which shift the artwork’s representation in the generator model’s feature space. Through training on multiple cloaked images, the generator model learns to associate the artist with a shifted artistic style, making it difficult for AI models to mimic the artist’s authentic style.
The effectiveness of Glaze was evaluated through user studies involving professional artists. The majority of surveyed artists found the perturbations to be minimal and not disruptive to the value of their art. The system successfully disrupted style mimicry by AI models, even when tested against real-world mimicry platforms. Glaze’s protection remained robust when artists shared significant amounts of artwork online.
In conclusion, Glaze offers a technical alternative to protect artists from style mimicry by AI models. Glaze has demonstrated its efficacy and usability through collaboration with professional artists and user studies. By applying minimal perturbations, Glaze empowers artists to counteract style mimicry and preserve their artistic uniqueness in the face of AI-generated art.
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Niharika is a Technical consulting intern at Marktechpost. She is a third year undergraduate, currently pursuing her B.Tech from Indian Institute of Technology(IIT), Kharagpur. She is a highly enthusiastic individual with a keen interest in Machine learning, Data science and AI and an avid reader of the latest developments in these fields.
