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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

Japanese

無形のスーパーマークは、絵の所有者をマークするために使用されます。私たちは、単純な攻撃が私たちがテストする最も強力な属性を削除することを示します。 攻撃探査機は、プレトレーニングインストールベースのレンダラーを使用して、ブラッシュオフを使用して、スーパーマークの絵を再描きます。私たちはこの攻撃をブラッシュオフと呼びます。それは水印の知識を必要としません、生成モデルはありません。 私たちは5つのスタイルから500の絵画で4つの水印をテストします。 VINEは、最近の水印を画像編集に生き残るために構築されたVINEですが、同一の削除レベルでは、少なくとも97.8%の検出で、私達の14の従来の歪み

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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