  Text-to-image diffusion models have emerged as an evolutionary for producing
creative content in image synthesis. Based on the impressive generation
abilities of these models, instruction-guided diffusion models can edit images
with simple instructions and input images. While they empower users to obtain
their desired edited images with ease, they have raised concerns about
unauthorized image manipulation. Prior research has delved into the
unauthorized use of personalized diffusion models; however, this problem of
instruction-guided diffusion models remains largely unexplored. In this paper,
we first propose a protection method EditShield against unauthorized
modifications from such models. Specifically, EditShield works by adding
imperceptible perturbations that can shift the latent representation used in
the diffusion process, tricking models into generating unrealistic images with
mismatched subjects. Our extensive experiments demonstrate EditShield's
effectiveness among synthetic and real-world datasets. Besides, we found that
EditShield performs robustly against various manipulation settings across
editing types and synonymous instruction phrases.
