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

TL;DR

This paper evaluates the ability of large vision-language models to detect image copyright infringement, introduces a new benchmark dataset, and analyzes their overfitting issues and failure cases.

Contribution

It constructs a comprehensive benchmark dataset for copyright detection and assesses the performance of state-of-the-art LVLMs on this task.

Findings

LVLMs tend to overfit, misclassifying non-infringing images as infringing.

The constructed dataset includes both infringement and ambiguous non-infringement samples.

Analysis of failure cases suggests potential directions for improving LVLM copyright detection.

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.