We employ an inversion-based approach to examine CLIP models. Our examination reveals that inverting CLIP models results in the generation of images that exhibit semantic alignment with the specified target prompts. We leverage these inverted images to gain insights into various aspects of CLIP models, such as their ability to blend concepts and inclusion of gender biases. We notably observe instances of NSFW (Not Safe For Work) images during model inversion. This phenomenon occurs even for semantically innocuous prompts, like "a beautiful landscape," as well as for prompts involving the names of celebrities.
Cite
@article{arxiv.2403.02580,
title = {What do we learn from inverting CLIP models?},
author = {Hamid Kazemi and Atoosa Chegini and Jonas Geiping and Soheil Feizi and Tom Goldstein},
journal= {arXiv preprint arXiv:2403.02580},
year = {2024}
}
Comments
Warning: This paper contains sexually explicit images and language, offensive visuals and terminology, discussions on pornography, gender bias, and other potentially unsettling, distressing, and/or offensive content for certain readers