Related papers: Circumventing Concept Erasure Methods For Text-to-Image Generative Models

Circumventing Concept Erasure Methods For Text-to-Image Generative Models

URL: http://arxiv.org/abs/2308.01508v2
Date: Sun, 8 Oct 2023 21:51:46 GMT
Title: Circumventing Concept Erasure Methods For Text-to-Image Generative Models
Authors: Minh Pham, Kelly O. Marshall, Niv Cohen, Govind Mittal, Chinmay Hegde
Abstract summary: Text-to-image generative models can produce photo-realistic images for an extremely broad range of concepts. These models have numerous drawbacks, including their potential to generate images featuring sexually explicit content. Various methods have been proposed in order to "erase" sensitive concepts from text-to-image models.
Score: 26.804057000265434
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Text-to-image generative models can produce photo-realistic images for an extremely broad range of concepts, and their usage has proliferated widely among the general public. On the flip side, these models have numerous drawbacks, including their potential to generate images featuring sexually explicit content, mirror artistic styles without permission, or even hallucinate (or deepfake) the likenesses of celebrities. Consequently, various methods have been proposed in order to "erase" sensitive concepts from text-to-image models. In this work, we examine five recently proposed concept erasure methods, and show that targeted concepts are not fully excised from any of these methods. Specifically, we leverage the existence of special learned word embeddings that can retrieve "erased" concepts from the sanitized models with no alterations to their weights. Our results highlight the brittleness of post hoc concept erasure methods, and call into question their use in the algorithmic toolkit for AI safety.

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