Parasite: A Steganography-based Backdoor Attack Framework for Diffusion Models
Journal:
arXiv
Published Date:
Apr 8, 2025
Abstract
Recently, the diffusion model has gained significant attention as one of the
most successful image generation models, which can generate high-quality images
by iteratively sampling noise. However, recent studies have shown that
diffusion models are vulnerable to backdoor attacks, allowing attackers to
enter input data containing triggers to activate the backdoor and generate
their desired output. Existing backdoor attack methods primarily focused on
target noise-to-image and text-to-image tasks, with limited work on backdoor
attacks in image-to-image tasks. Furthermore, traditional backdoor attacks
often rely on a single, conspicuous trigger to generate a fixed target image,
lacking concealability and flexibility. To address these limitations, we
propose a novel backdoor attack method called "Parasite" for image-to-image
tasks in diffusion models, which not only is the first to leverage
steganography for triggers hiding, but also allows attackers to embed the
target content as a backdoor trigger to achieve a more flexible attack.
"Parasite" as a novel attack method effectively bypasses existing detection
frameworks to execute backdoor attacks. In our experiments, "Parasite" achieved
a 0 percent backdoor detection rate against the mainstream defense frameworks.
In addition, in the ablation study, we discuss the influence of different
hiding coefficients on the attack results. You can find our code at
https://anonymous.4open.science/r/Parasite-1715/.
