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BrushOff: Stroke-Based Re-rendering as a Watermark Removal Attack on Painting

Hao Chen, Junnan Xu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26682 · cited by 0 works (OpenAlex) · open-access PDF

Portuguese

Sondas invisíveis são usadas para marcar quem possui uma pintura. Mostramos que um simples ataque remove a propriedade mais robusta que testamos. A sonda de ataque redescobre a pintura marcada com pulseiras, usando um render baseado em pulseira pré-treinado. Nós chamamos este ataque BrushOff. Ele não precisa de treinamento, nenhum conhecimento da marca d'água, e nenhum modelo gerador. Nós testamos quatro marcas d'água em 500 pinturas de cinco estilos. VINE, uma marca d'água recente construída para sobreviver à edição de imagem, sobrevive a todas as 14 distorções convencionais que testamos, com pelo menos 97,8% de detecção. Benpainting reduz sua detecção para 1,8%, enquanto a saída mantém uma pontuação de qualidade sem referência próxima da das pinturas limpas

Machine translation (Meta M2M100); the English original follows.

Abstract

Invisible watermarks are used to mark who owns a painting. We show that a simple attack removes the most robust one we test. The attack redraws the watermarked painting with brush strokes, using a pretrained stroke-based renderer. We call this attack BrushOff. It needs no training, no knowledge of the watermark, and no generative model. We test four watermarks on 500 paintings from five styles. VINE, a recent watermark built to survive image editing, survives all 14 conventional distortions we test, with at least 97.8% detection. Repainting lowers its detection to 1.8%, while the output keeps a no-reference quality score close to that of the clean paintings. Diffusion regeneration can also remove VINE, but at the same removal level it moves the image twice as far from the painting in DreamSim distance, and it changes what the painting shows. Because the renderer draws a countable number of strokes, we can control the attack strength step by step. Finally, we test what kind of signal survives. A simple probe that stores bits in the relative brightness of neighboring regions survives repainting, while a probe that uses absolute brightness does not. The same probe fails under diffusion regeneration, while VINE fails under repainting. Robustness to these two attacks is therefore a separate property, and benchmarks should test both.

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Topics: Computer science, Robustness (evolution), Artificial intelligence, Painting, Watermark, Computer vision

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