Rickrolling the Artist: Injecting Backdoors into Text Encoders for Text-to-Image Synthesis

Understand

While text-to-image synthesis currently enjoys great popularity among researchers and the general public, the security of these models has been neglected so far.

• Many text-guided image generation models rely on pre-trained text encoders from external sources, and their users trust that the retrieved models will behave as promised.
• Unfortunately, this might not be the case.
• We introduce backdoor attacks against text-guided generative models and demonstrate that their text encoders pose a major tampering risk.