基于未训练神经网络先验的化妆引导人脸隐私保护
Makeup-Guided Facial Privacy Protection via Untrained Neural Network Priors
AI总结：
针对现有化妆对抗攻击方法依赖大规模数据集且存在性别偏差的问题，提出一种基于未训练神经网络先验的测试时优化方法，通过潜在空间对应模块和条件化妆层解码器生成对抗性化妆图像以保护人脸隐私，并扩展至视频，在基准数据集和商业系统上均表现优异。
摘要：
基于深度学习的人脸识别（FR）系统在未经用户同意的情况下追踪用户，带来了重大的隐私风险。尽管对抗攻击可以保护隐私，但它们通常会产生可见的伪影，从而损害用户体验。为了缓解这一问题，最近的人脸隐私保护方法提倡将对抗性噪声嵌入到自然的化妆风格中。然而，这些方法需要在大规模化妆数据集上进行训练，而这些数据集并不总是容易获取。此外，这些方法还存在数据集偏差问题。例如，在主要包含女性人脸的化妆数据上训练，可能会削弱对男性人脸的保护效果。为了解决这些问题，我们提出了一种测试时优化方法，该方法仅优化一个未训练的神经网络，以对抗方式将参考图像的化妆风格迁移到源图像。我们引入了两个关键模块：一个在潜在空间中对齐参考图像和源图像区域的对应模块，以及一个带有条件化妆层的解码器。这个未训练的解码器通过精心设计的结构一致性和化妆一致性损失进行优化，生成一张受保护图像，该图像在视觉上类似于源图像，但包含对抗性化妆以欺骗FR模型。由于我们的方法不依赖于使用化妆人脸数据集进行训练，因此它避免了潜在的男性/女性数据集偏差，同时提供了有效的保护。我们通过利用时间相关性，进一步将所提出的方法扩展到视频。在基准数据集上的实验表明，该方法在人脸验证和识别任务中表现出卓越的性能，并且对商业FR系统具有有效性。我们的代码和模型将发布于 https://github.com/fahadshamshad/deep-facial-privacy-prior
英文摘要：
Deep learning-based face recognition (FR) systems pose significant privacy risks by tracking users without their consent. While adversarial attacks can protect privacy, they often produce visible artifacts compromising user experience. To mitigate this issue, recent facial privacy protection approaches advocate embedding adversarial noise into the natural looking makeup styles. However, these methods require training on large-scale makeup datasets that are not always readily available. In addition, these approaches also suffer from dataset bias. For instance, training on makeup data that predominantly contains female faces could compromise protection efficacy for male faces. To handle these issues, we propose a test-time optimization approach that solely optimizes an untrained neural network to transfer makeup style from a reference to a source image in an adversarial manner. We introduce two key modules: a correspondence module that aligns regions between reference and source images in latent space, and a decoder with conditional makeup layers. The untrained decoder, optimized via carefully designed structural and makeup consistency losses, generates a protected image that resembles the source but incorporates adversarial makeup to deceive FR models. As our approach does not rely on training with makeup face datasets, it avoids potential male/female dataset biases while providing effective protection. We further extend the proposed approach to videos by leveraging on temporal correlations. Experiments on benchmark datasets demonstrate superior performance in face verification and identification tasks and effectiveness against commercial FR systems. Our code and models will be available at https://github.com/fahadshamshad/deep-facial-privacy-prior
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
