BAGM: A Backdoor Attack for Manipulating Text-to-Image Generative Models

Understand

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.