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arxiv: 2605.03547 · v1 · submitted 2026-05-05 · 💻 cs.CV · cs.AI

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Erase Persona, Forget Lore: Benchmarking Multimodal Copyright Unlearning in Large Vision Language Models

JuneHyoung Kwon , JungMin Yun , YoungBin Kim
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Pith reviewed 2026-05-07 17:58 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords unlearninglvlmscontentcopyrightcovubenchevaluationmultimodaldata
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The pith

CoVUBench is the first benchmark framework for evaluating multimodal copyright unlearning in LVLMs via synthetic data, systematic variations, and a dual protocol for forgetting efficacy and utility preservation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large vision-language models trained on internet data often memorize and can regenerate copyrighted images such as characters or logos. Machine unlearning aims to remove that specific knowledge after training, but it is hard to check whether the unlearning worked without breaking the rest of the model. Existing tests are limited, so the authors created CoVUBench. It generates safe synthetic images that mimic copyrighted material, applies many visual changes like different compositions and styles, and measures both whether the forbidden content is forgotten and whether the model still performs normally on unrelated tasks.

Core claim

we introduce the CoVUBench benchmark, the first framework specifically designed for evaluating copyright content unlearning in LVLMs. CoVUBench utilizes procedurally generated, legally safe synthetic data coupled with systematic visual variations spanning compositional changes and diverse domain manifestations to ensure realistic and robust evaluation of unlearning generalization.

Load-bearing premise

That procedurally generated synthetic data with visual variations sufficiently captures the nuances of cross-modal concept erasure for real copyrighted content, allowing the benchmark to generalize beyond the synthetic cases.

Figures

Figures reproduced from arXiv: 2605.03547 by JuneHyoung Kwon, JungMin Yun, YoungBin Kim.

Figure 1
Figure 1. Figure 1: Overview of the CoVUBench generation pipeline. sitional variation—presenting the concept in varied visual layouts (e.g., different backgrounds, views, and scenes)—and domain manifestation, where the concept appears as real-world derivatives (e.g., a character appearing as a 3D action figure or a t-shirt print). Our goal is to synthesize novel copyright concepts within diverse visual contexts, guided by sim… view at source ↗
Figure 2
Figure 2. Figure 2: A comparison of single-modal (text-only) view at source ↗
Figure 3
Figure 3. Figure 3: The impact of increasing the forget ratio (5% to 20%) on the performance of five unlearning view at source ↗
Figure 4
Figure 4. Figure 4: Performance comparison across ’logo’ and ’character’ domains for five unlearning base￾lines, evaluated on Fluency, Specificity and Effi￾cacy. GD and DPO remain consistently ineffective at era￾sure, failing to forget either domain. We conjecture that NPO more effectively suppresses the complex textual ’lore’ of characters than the atomic visual￾name link of logos, but this broader suppression of ’lore’ caus… view at source ↗
read the original abstract

Large Vision-Language Models (LVLMs), trained on web-scale data, risk memorizing and regenerating copyrighted visual content such as characters and logos, creating significant challenges. Machine unlearning offers a path to mitigate these risks by removing specific content post-training, but evaluating its effectiveness, especially in the complex multimodal setting of LVLMs, remains an open problem. Current evaluation methods often lack robustness or fail to capture the nuances of cross-modal concept erasure. To address this critical gap, we introduce the CoVUBench benchmark, the first framework specifically designed for evaluating copyright content unlearning in LVLMs. CoVUBench utilizes procedurally generated, legally safe synthetic data coupled with systematic visual variations spanning compositional changes and diverse domain manifestations to ensure realistic and robust evaluation of unlearning generalization. Our comprehensive multimodal evaluation protocol assesses both forgetting efficacy from the copyright holder perspective and the preservation of general model utility from the deployer viewpoint. By rigorously measuring this crucial trade-off, CoVUBench provides a standardized tool to advance the development of responsible and effective unlearning methods for LVLMs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The benchmark rests on the assumption that synthetic data can proxy real copyrighted multimodal content; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Procedurally generated synthetic data with compositional and domain variations can serve as a realistic proxy for evaluating unlearning of real copyrighted visual content in LVLMs
    Invoked to justify the benchmark's claim of realistic and robust generalization evaluation.

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