mlsanityData to 14 Oct 2026

Bridging the Copyright Gap: Do Large Vision-Language Models Recognize and Respect Copyrighted Content?

arXiv 2512.21871 ↗First posted 26 Dec 2025cs.CL cs.AI cs.CRcited by 10

On the map: LLM reasoning → Multimodal reasoning

also in 1 more
Abstract (arXiv)

Large vision-language models (LVLMs) have achieved remarkable advancements in multimodal reasoning tasks. However, their widespread accessibility raises critical concerns about potential copyright infringement. Will LVLMs accurately recognize and comply with copyright regulations when encountering copyrighted content (i.e., user input, retrieved documents) in the context? Failure to comply with copyright regulations may lead to serious legal and ethical consequences, particularly when LVLMs generate responses based on copyrighted materials (e.g., retrieved book experts, news reports). In this paper, we present a comprehensive evaluation of various LVLMs, examining how they handle copyrighted content -- such as book excerpts, news articles, music lyrics, and code documentation when they are presented as visual inputs. To systematically measure copyright compliance, we introduce a large-scale benchmark dataset comprising 50,000 multimodal query-content pairs designed to evaluate how effectively LVLMs handle queries that could lead to copyright infringement. Given that real-world copyrighted content may or may not include a copyright notice, the dataset includes query-content pairs in two distinct scenarios: with and without a copyright notice. For the former, we extensively cover four types of copyright notices to account for different cases. Our evaluation reveals that even state-of-the-art closed-source LVLMs exhibit significant deficiencies in recognizing and respecting the copyrighted content, even when presented with the copyright notice. To solve this limitation, we introduce a novel tool-augmented defense framework for copyright compliance, which reduces infringement risks in all scenarios. Our findings underscore the importance of developing copyright-aware LVLMs to ensure the responsible and lawful use of copyrighted content.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Placed in Multimodal reasoning, one of 17 directions in LLM reasoning. Builds on 9 of its 22 references that are in the map. First posted on arXiv 26 Dec 2025.

Builds on

9 of 22 references.

References · 9

  1. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  2. 2023Improved Baselines with Visual Instruction Tuningcited by 4,418
  3. 2023Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversationscited by 1,207
  4. 2024ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Toolscited by 1,121
  5. 2024VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documentscited by 245
  6. 2023MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learningcited by 172
  7. 2023Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4cited by 125
  8. 2024SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generationcited by 34
  9. 2024Do LLMs Know to Respect Copyright Notice?cited by 13

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 10.

10 papers.

  1. 2026ACIArena: Toward Unified Evaluation for Agent Cascading Injectioncited by 7
  2. 2026TSEmbed: Unlocking Task Scaling in Universal Multimodal Embeddingscited by 6
  3. 2026Compiling Activation Steering into Weights via Null-Space Constraints for Stealthy Backdoorscited by 4
  4. 2026Visual-Noise Guided In-Context Distillation for Multimodal Large Language Model Unlearningcited by 3
  5. 2026"I See What You Did There": Can Large Vision-Language Models Understand Multimodal Puns?cited by 2
  6. 2026Spotlight and Shadow: Attention-Guided Dual-Anchor Introspective Decoding for MLLM Hallucination Mitigationcited by 1
  7. 2026DNF: Dual-Layer Nested Fingerprinting for Large Language Model Intellectual Property Protectioncited by 0
  8. 2026Demystifying the Optimal Fair Classifier in Multi-Class Classificationcited by 0
  9. 2026The Boy Who Cried Wolf: Adversarial Misclassification of Safe Inputs as Unsafe in Multimodal Guardrailscited by 0
  10. 2026CUSE: Fine-Grained Multimodal Unlearning with Counterfactual Guidance and On-Policy Self-Distillationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Can Large Vision-Language Models Detect Images Copyright Infringement from GenAI?
  2. 2026Erase Persona, Forget Lore: Benchmarking Multimodal Copyright Unlearning in Large Vision Language Models
  3. 2024Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?
  4. 2026Agentic Evaluation of Copyright Law Compliance
  5. 2025CopyJudge: Automated Copyright Infringement Identification and Mitigation in Text-to-Image Diffusion Models
  6. 2026Probing Stylistic Appropriation using Large Language Models: An Evaluation Framework for Copyright Infringement under EU Law
  7. 2024Avoiding Copyright Infringement via Large Language Model Unlearning
  8. 2025Tracking the Copyright of Large Vision-Language Models through Parameter Learning Adversarial Images