Can Large Vision-Language Models Detect Images Copyright Infringement from GenAI?
Journal:
arXiv
Published Date:
Feb 23, 2025
Abstract
Generative AI models, renowned for their ability to synthesize high-quality
content, have sparked growing concerns over the improper generation of
copyright-protected material. While recent studies have proposed various
approaches to address copyright issues, the capability of large vision-language
models (LVLMs) to detect copyright infringements remains largely unexplored. In
this work, we focus on evaluating the copyright detection abilities of
state-of-the-art LVLMs using a various set of image samples. Recognizing the
absence of a comprehensive dataset that includes both IP-infringement samples
and ambiguous non-infringement negative samples, we construct a benchmark
dataset comprising positive samples that violate the copyright protection of
well-known IP figures, as well as negative samples that resemble these figures
but do not raise copyright concerns. This dataset is created using advanced
prompt engineering techniques. We then evaluate leading LVLMs using our
benchmark dataset. Our experimental results reveal that LVLMs are prone to
overfitting, leading to the misclassification of some negative samples as
IP-infringement cases. In the final section, we analyze these failure cases and
propose potential solutions to mitigate the overfitting problem.
