  The rise in popularity of text-to-image generative artificial intelligence
(AI) has attracted widespread public interest. We demonstrate that this
technology can be attacked to generate content that subtly manipulates its
users. We propose a Backdoor Attack on text-to-image Generative Models (BAGM),
which upon triggering, infuses the generated images with manipulative details
that are naturally blended in the content. Our attack is the first to target
three popular text-to-image generative models across three stages of the
generative process by modifying the behaviour of the embedded tokenizer, the
language model or the image generative model. Based on the penetration level,
BAGM takes the form of a suite of attacks that are referred to as surface,
shallow and deep attacks in this article. Given the existing gap within this
domain, we also contribute a comprehensive set of quantitative metrics designed
specifically for assessing the effectiveness of backdoor attacks on
text-to-image models. The efficacy of BAGM is established by attacking
state-of-the-art generative models, using a marketing scenario as the target
domain. To that end, we contribute a dataset of branded product images. Our
embedded backdoors increase the bias towards the target outputs by more than
five times the usual, without compromising the model robustness or the
generated content utility. By exposing generative AI's vulnerabilities, we
encourage researchers to tackle these challenges and practitioners to exercise
caution when using pre-trained models. Relevant code, input prompts and
supplementary material can be found at https://github.com/JJ-Vice/BAGM, and the
dataset is available at:
https://ieee-dataport.org/documents/marketable-foods-mf-dataset.
  Keywords: Generative Artificial Intelligence, Generative Models,
Text-to-Image generation, Backdoor Attacks, Trojan, Stable Diffusion.
