Can Large Vision-Language Models Detect Images Copyright Infringement from GenAI?

• URL: http://arxiv.org/abs/2502.16618v1
• Date: Sun, 23 Feb 2025 15:41:12 GMT
• Title: Can Large Vision-Language Models Detect Images Copyright Infringement from GenAI?
• Authors: Qipan Xu, Zhenting Wang, Xiaoxiao He, Ligong Han, Ruixiang Tang,
• Abstract summary: We focus on evaluating the copyright detection abilities of state-of-the-art LVLMs using a various set of image samples.<n>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.<n>Our experimental results reveal that LVLMs are prone to overfitting, leading to the misclassification of some negative samples as IP-infringement cases.
• Score: 22.898606027486593
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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