Large Vision-Language Models Demonstrate Copyright Recognition with Comprehensive Evaluation of Content

The increasing power of artificial intelligence raises important questions about intellectual property, and a new study investigates whether large vision-language models respect copyright restrictions. Naen Xu, Jinghuai Zhang from the University of California, Los Angeles, and Changjiang Li, alongside Hengyu An, Chunyi Zhou from Zhejiang University, and Jun Wang from OPPO Research Institute, present a comprehensive evaluation of these models’ ability to recognise and adhere to copyright regulations when processing visual information. Their work addresses a critical gap in understanding, as these models become increasingly capable of generating content based on user inputs and existing materials, potentially leading to legal and ethical issues. The team developed a large benchmark dataset, containing 50,000 examples, to assess how effectively these models handle copyrighted content, including books, news, music and code, both with and without explicit copyright notices, and the results reveal significant shortcomings in current state-of-the-art systems. This research highlights the urgent need for copyright-aware AI and introduces a new framework to mitigate the risk of infringement, paving the way for responsible development and deployment of these powerful technologies.

Copyrighted Data Sources For Dataset Creation

This document details the data sources used to create a dataset, explicitly stating that all materials are copyrighted. This declaration serves legal and ethical purposes, demonstrating responsible data handling practices and compliance with copyright laws. The dataset encompasses a diverse range of content, including books, news articles, music lyrics, and code documentation, suggesting the aim is to create a comprehensive and well-rounded language model. Data originates from sources such as BBC, CNN, Spotify, Wikipedia, Hugging Face, and the Python Package Index (PyPI), with a notable focus on resources central to machine learning and Python development. The inclusion of code documentation indicates a specific interest in training models capable of understanding and generating code. While the method of data collection is not detailed, it likely involved web scraping, API access, or licensed agreements, and the document emphasizes the importance of respecting copyright restrictions when working with large language models and artificial intelligence.

Copyright Compliance Evaluation of Vision-Language Models

Researchers have pioneered a comprehensive methodology to evaluate copyright compliance in large vision-language models (LVLMs), addressing a significant gap in understanding how these systems handle copyrighted material. They formulated a quantitative scoring function to objectively measure a model’s response to multimodal content and textual queries, assessing compliance based on lexical overlap, semantic similarity, and refusal rates when presented with potentially infringing prompts. To systematically assess LVLMs, the team constructed a large-scale benchmark dataset of 50,000 multimodal query-content pairs, drawing from books, news articles, music lyrics, and code documentation sourced from platforms like Goodreads, BBC, Spotify, Hugging Face Docs, and PyPI. The dataset incorporates scenarios with and without explicit copyright notices, reflecting real-world conditions and accounting for variations in notice presentation. Experiments compared model performance with and without notices, and scientists developed CopyGuard, a novel defense framework to enhance copyright compliance and improve the overall dataset-level compliance score.

LVLM Copyright Compliance Benchmark Reveals Deficiencies

Scientists have achieved a breakthrough in evaluating the copyright compliance of large vision-language models (LVLMs), introducing a large-scale benchmark dataset of 50,000 multimodal query-content pairs. This dataset is designed to measure how effectively LVLMs handle queries that could potentially lead to copyright infringement, accounting for copyrighted content with and without explicit notices. Experiments revealed that even state-of-the-art LVLMs exhibit substantial deficiencies in recognizing and respecting copyrighted material, even when presented with clear notices. The team quantified compliance using a scoring function that assessed lexical overlap, semantic similarity, and refusal rates, demonstrating consistent failures across various models. To address these limitations, they developed CopyGuard, a tool-augmented defense framework that effectively prevents the generation of copyrighted content, substantially reducing infringement risks and safeguarding intellectual property.

Vision-Language Models And Copyright Compliance

This research investigates the ability of large vision-language models to recognize and respect copyrighted material, a crucial consideration as these models become more widely used. The team developed a benchmark dataset, comprising over 50,000 multimodal examples, to systematically assess how these models handle copyrighted content, including books, news articles, music, and code, both with and without explicit copyright notices. Through extensive evaluation, the researchers demonstrated that current models frequently fail to comply with copyright regulations, potentially increasing legal and ethical risks. To address this, they introduced CopyGuard, a framework designed to function as a defensive measure against copyright infringement, demonstrably reducing the generation of copyrighted material. While introducing a slight delay due to verification, the benefits of mitigating legal risks often outweigh this impact, and future work could focus on optimizing the process and expanding the dataset to include a wider range of materials and languages.