My Art My Choice: Adversarial Protection Against Unruly AI
Anthony Rhodes Ram Bhagat Umur Aybars Ciftci Ilke Demir
Generative AI is on the rise, enabling everyone to produce realistic content
via publicly available interfaces. Especially for guided image generation,
diffusion models are changing the creator economy by producing high quality low
cost content. In parallel, artists are rising against unruly AI, since their
artwork are leveraged, distributed, and dissimulated by large generative
models. Our approach, My Art My Choice (MAMC), aims to empower content owners
by protecting their copyrighted materials from being utilized by diffusion
models in an adversarial fashion. MAMC learns to generate adversarially
perturbed "protected" versions of images which can in turn "break" diffusion
models. The perturbation amount is decided by the artist to balance distortion
vs. protection of the content. MAMC is designed with a simple UNet-based
generator, attacking black box diffusion models, combining several losses to
create adversarial twins of the original artwork. We experiment on three
datasets for various image-to-image tasks, with different user control values.
Both protected image and diffusion output results are evaluated in visual,
noise, structure, pixel, and generative spaces to validate our claims. We
believe that MAMC is a crucial step for preserving ownership information for AI
generated content in a flawless, based-on-need, and human-centric way.