The Many Faces of AI Art: Self-Poisoning Generative Models

Abstract

As AI-generated content becomes more prevalent, the risk of generative models consuming and regenerating their own outputs in a self-consuming loop increases. This study explores the phenomenon of self-poisoning in generative models, an iterative process where AI-generated outputs are repeatedly used as input for further generations. Using a dataset of artworks by renowned artists, the process involves captioning the artworks, generating images from the captions, and repeating this cycle. The research focuses on the impact of self-poisoning on creative novelty, evaluated through content and visual novelty metrics. The findings reveal that while self-poisoning introduces novel elements in both content and visuals, it simultaneously degrades the quality of the generated artifacts over time. Generative models struggle to maintain the complexity and creativity of the original artworks, leading to outputs that converge on certain themes and realistic styles. This study contributes to a broader understanding of AI's role in art and highlights potential limitations posed by iterative generative processes.