Face image manipulation methods, despite having many beneficial applications in computer graphics, can also raise concerns by affecting an individual's privacy or spreading disinformation. In this work, we propose a proactive defense to prevent face manipulation from happening in the first place. To this end, we introduce a novel data-driven approach that produces image-specific perturbations which are embedded in the original images. The key idea is that these protected images prevent face manipulation by causing the manipulation model to produce a predefined manipulation target (uniformly colored output image in our case) instead of the actual manipulation. Compared to traditional adversarial attacks that optimize noise patterns for each image individually, our generalized model only needs a single forward pass, thus running orders of magnitude faster and allowing for easy integration in image processing stacks, even on resource-constrained devices like smartphones. In addition, we propose to leverage a differentiable compression approximation, hence making generated perturbations robust to common image compression. We further show that a generated perturbation can simultaneously prevent against multiple manipulation methods.

本文提出了一种新的数据驱动方法，通过将保护图像嵌入原始图像来防止面部操纵，生成的扰动对常见图像压缩具有鲁棒性，同时引入关注度融合机制，从而提高保护效果。

TAFIM：针对面部图像篡改的定向对抗攻击