Nepotistically Trained Generative-AI Models Collapse
Matyas Bohacek Hany Farid
Trained on massive amounts of human-generated content, AI (artificial
intelligence) image synthesis is capable of reproducing semantically coherent
images that match the visual appearance of its training data. We show that when
retrained on even small amounts of their own creation, these generative-AI
models produce highly distorted images. We also show that this distortion
extends beyond the text prompts used in retraining, and that once poisoned, the
models struggle to fully heal even after retraining on only real images.