Linkang Du (Zhejiang University), Zheng Zhu (Zhejiang University), Min Chen (CISPA Helmholtz Center for Information Security), Shouling Ji (Zhejiang University), Peng Cheng (Zhejiang University), Jiming Chen (Zhejiang University), Zhikun Zhang (Stanford University)
The text-to-image models based on diffusion processes, capable of transforming text descriptions into detailed images, have widespread applications in art, design, and beyond, such as DALL-E, Stable Diffusion, and Midjourney. However, they enable users without artistic training to create artwork comparable to professional quality, leading to concerns about copyright infringement. To tackle these issues, previous works have proposed strategies such as adversarial perturbation-based and watermarking-based methods. The former involves introducing subtle changes to disrupt the image generation process, while the latter involves embedding detectable marks in the artwork. The existing methods face limitations such as requiring modifications of the original image, being vulnerable to image pre-processing, and facing difficulties in applying them to the published artwork.
To this end, we propose a new paradigm, called StyleAuditor, for artistic style auditing. StyleAuditor identifies if a suspect model has been fine-tuned using a specific artist’s artwork by analyzing style-related features. Specifically, StyleAuditor employs a style extractor to obtain the multi-granularity style representations and treats artwork as samples of an artist’s style. Then, StyleAuditor queries a trained discriminator to gain the auditing decisions. The results of the experiment on the artwork of thirty artists demonstrate the high accuracy of StyleAuditor, with an auditing accuracy of over 90% and a false positive rate of less than 1.3%.