通过纹理扰动提前防御深度伪造，让假脸显出破绽。
Defending Deepfake via Texture Feature Perturbation
- 利用局部二值模式提取面部纹理特征，精准定位可扰动区域。
- 在低感知显著区插入不可见扰动，使生成假脸出现明显视觉缺陷。
- 对多种攻击模型有效，适合部署于实时防伪系统。
深度伪造技术的快速发展严重威胁社会信任与信息安全。现有检测方法多依赖被动分析，难以应对高质量伪造内容；而主动防御则通过在图像编辑前嵌入不可见信号实现预防。本文提出一种基于面部纹理特征的主动检测方法：由于人眼对平滑区域扰动更敏感，我们在纹理区域（低感知显著性区域）中引入不可见扰动，仅在关键纹理区施加局部扰动，避免非纹理区产生多余噪声。该纹理引导扰动框架首先通过局部二值模式（LBP）提取初步纹理特征，再采用双模型注意力机制生成并优化扰动。在CelebA-HQ和LFW数据集上的实验表明，该方法能有效破坏深度伪造生成过程，并在多种攻击模型下产生明显的视觉缺陷，为前瞻性深度伪造检测提供了高效且可扩展的解决方案。
原文摘要 · Abstract (English)
The rapid development of Deepfake technology poses severe challenges to social trust and information security. While most existing detection methods primarily rely on passive analyses, due to unresolvable high-quality Deepfake contents, proactive defense has recently emerged by inserting invisible signals in advance of image editing. In this paper, we introduce a proactive Deepfake detection approach based on facial texture features. Since human eyes are more sensitive to perturbations in smooth regions, we invisibly insert perturbations within texture regions that have low perceptual saliency, applying localized perturbations to key texture regions while minimizing unwanted noise in non-textured areas. Our texture-guided perturbation framework first extracts preliminary texture features via Local Binary Patterns (LBP), and then introduces a dual-model attention strategy to generate and optimize texture perturbations. Experiments on CelebA-HQ and LFW datasets demonstrate the promising performance of our method in distorting Deepfake generation and producing obvious visual defects under multiple attack models, providing an efficient and scalable solution for proactive Deepfake detection.
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