AI vs. copyright: How large vision-language models are changing IP protection

Generative AI models have demonstrated remarkable capabilities in creating high-quality images. However, these models may inadvertently reproduce copyrighted content due to memorization of training data or the influence of user prompts. Current mitigation strategies focus on reducing memorization through differential privacy techniques and refining prompt engineering to discourage explicit IP replication. However, these approaches primarily target the generation process, leaving a gap in the detection of infringing content that has already been created and circulated.

The rapid advancement of generative artificial intelligence (GenAI) has raised new concerns about intellectual property (IP) rights. AI-generated images, often inspired by copyrighted characters and artistic styles, challenge the boundaries of ownership, legality, and fair use. While efforts to mitigate copyright issues in generative models have been explored, the effectiveness of large vision-language models (LVLMs) in detecting copyright infringement remains an underexplored area.

A recent study titled "Can Large Vision-Language Models Detect Images Copyright Infringement from GenAI?" by Qipan Xu, Zhenting Wang, Xiaoxiao He, Ligong Han, and Ruixiang Tang from Rutgers University systematically evaluates the ability of LVLMs to identify copyright violations. By constructing a benchmark dataset of IP-infringing and non-infringing samples, the study provides insights into the strengths and limitations of AI-driven copyright detection.

Challenge of AI-generated copyright infringement

Generative AI models have demonstrated remarkable capabilities in creating high-quality images. However, these models may inadvertently reproduce copyrighted content due to memorization of training data or the influence of user prompts. Current mitigation strategies focus on reducing memorization through differential privacy techniques and refining prompt engineering to discourage explicit IP replication. However, these approaches primarily target the generation process, leaving a gap in the detection of infringing content that has already been created and circulated.

The study highlights that existing copyright detection methods rely heavily on manual human review or traditional image-matching algorithms, which struggle with nuanced variations and stylistic modifications. To address this, the researchers propose leveraging large vision-language models (LVLMs) to assess whether AI-generated images infringe on copyrighted material. LVLMs, which integrate textual and visual data processing, have shown promise in cross-modal reasoning tasks such as image classification and content moderation. However, their effectiveness in copyright detection remains largely untested.

Constructing a benchmark dataset for copyright detection

To evaluate the capabilities of LVLMs, the researchers developed a benchmark dataset comprising both positive samples (images that clearly infringe on well-known IP) and negative samples (images that resemble famous characters but do not violate copyright laws). This dataset includes AI-generated representations of five iconic IP-protected characters: Iron Man, Batman, Spider-Man, Superman, and Super Mario. By curating images that range from direct replications to stylistically inspired works, the dataset provides a comprehensive testbed for copyright infringement detection.

The study employs two primary methods to test LVLMs: in-context learning (ICL) and zero-shot learning (ZSL). In ICL, models are provided with labeled examples of infringing and non-infringing content before making predictions, allowing them to learn from context. In ZSL, models must classify images without prior exposure to labeled examples. By comparing the results of these approaches, the researchers assess the adaptability and precision of different LVLMs in copyright detection.

Results: Can AI accurately detect copyright violations?

The findings reveal that while LVLMs exhibit strong recall - meaning they successfully identify most instances of copyright infringement - they suffer from high false positive rates, often misclassifying non-infringing images as violations. This suggests that LVLMs are prone to overfitting, focusing on superficial visual similarities rather than understanding deeper conceptual differences between inspiration and infringement.

Among the tested models, GPT-4o mini demonstrated the highest accuracy in IP detection. However, all models showed a tendency to classify ambiguous cases as infringements, particularly when images shared key visual elements with the original IP characters (e.g., Iron Man’s red-and-gold armor or Batman’s cape and mask). This highlights a critical limitation: LVLMs may struggle with contextual judgment, leading to overly conservative copyright enforcement.

To address these challenges, the researchers propose several mitigation strategies, including contrastive learning techniques that refine models’ ability to differentiate between lawful artistic inspiration and unauthorized replication. Additionally, the study underscores the need for improved legal and ethical AI frameworks to establish clearer guidelines for copyright compliance in AI-generated content.

Future Directions: Improving AI for Copyright Protection

While this study presents an important step in evaluating LVLMs for copyright detection, several challenges remain. The high false positive rate indicates that current AI models require more nuanced training data and improved contextual awareness to distinguish between infringement and fair use. Future research should focus on:

• Fine-tuning AI models using contrastive learning to reduce misclassification of non-infringing images.
• Developing hybrid detection frameworks that combine LVLMs with traditional watermarking and content recognition techniques.
• Implementing ethical AI governance to ensure transparency and fairness in automated copyright enforcement.

Ultimately, this study underscores the complexity of AI-driven copyright moderation. While LVLMs offer promising tools for identifying IP violations, their current limitations suggest that human oversight remains essential in the evaluation process. As AI continues to evolve, balancing innovation with ethical and legal considerations will be critical in shaping the future of copyright protection in the digital age.