精准拦截恶意图像编辑，保护内容安全同时不干扰正常操作。
TarPro: Targeted Protection against Malicious Image Editing
- 通过语义感知约束，仅干扰恶意编辑内容。
- 生成轻量级不可察觉的扰动，提升防护稳定性。
- 适合需要内容安全的图像编辑平台使用。
图像编辑技术的快速发展引发了其被滥用于生成不适宜工作场所（NSFW）内容的担忧。为此，亟需一种既能阻止恶意编辑又保留正常编辑能力的靶向保护机制。然而，现有方法要么无差别地破坏所有编辑，仍允许部分有害内容生成。本文提出TarPro，一种靶向保护框架，可在保持良性修改的同时防止恶意编辑。该方法通过语义感知约束仅干扰恶意内容，并结合轻量级扰动生成器，实现更稳定、难以察觉且鲁棒的图像保护。大量实验表明，TarPro在保护效果上优于现有方法，同时对正常编辑影响极小。结果验证了TarPro在安全可控图像编辑中的实际可行性。
原文摘要 · Abstract (English)
The rapid advancement of image editing techniques has raised concerns about their misuse for generating Not-Safe-for-Work (NSFW) content. This necessitates a targeted protection mechanism that blocks malicious edits while preserving normal editability. However, existing protection methods fail to achieve this balance, as they indiscriminately disrupt all edits while still allowing some harmful content to be generated. To address this, we propose TarPro, a targeted protection framework that prevents malicious edits while maintaining benign modifications. TarPro achieves this through a semantic-aware constraint that only disrupts malicious content and a lightweight perturbation generator that produces a more stable, imperceptible, and robust perturbation for image protection. Extensive experiments demonstrate that TarPro surpasses existing methods, achieving a high protection efficacy while ensuring minimal impact on normal edits. Our results highlight TarPro as a practical solution for secure and controlled image editing.
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