With the wide application of face recognition systems, there is rising concern that original face images could be exposed to malicious intents and consequently cause personal privacy breaches. This paper presents DuetFace, a novel privacy-preserving face recognition method that employs collaborative inference in the frequency domain. Starting from a counterintuitive discovery that face recognition can achieve surprisingly good performance with only visually indistinguishable high-frequency channels, this method designs a credible split of frequency channels by their cruciality for visualization and operates the server-side model on non-crucial channels. However, the model degrades in its attention to facial features due to the missing visual information. To compensate, the method introduces a plug-in interactive block to allow attention transfer from the client-side by producing a feature mask. The mask is further refined by deriving and overlaying a facial region of interest (ROI). Extensive experiments on multiple datasets validate the effectiveness of the proposed method in protecting face images from undesired visual inspection, reconstruction, and identification while maintaining high task availability and performance. Results show that the proposed method achieves a comparable recognition accuracy and computation cost to the unprotected ArcFace and outperforms the state-of-the-art privacy-preserving methods. The source code is available at https://github.com/Tencent/TFace/tree/master/recognition/tasks/duetface.
翻译:随着面部识别系统的广泛应用,人们日益担心原始面部图像可能会暴露于恶意意图,从而导致个人隐私受损。本文展示了“杜埃特脸”这一新型的隐私保护面部识别方法,在频率域内采用了协作推理。从反直觉发现,面对的识别能够取得令人惊讶的良好性能,只有视觉上无法辨别的高频频道,这种方法设计了可靠的频率频道分割,视像化至关重要,并在非封闭的频道上运行服务器-侧面模型。然而,模型由于缺少视觉信息,对面部特征的注意力降低。为补偿起见,该方法引入了一个插座互动块,通过生成功能掩体,使客户方的注意力得以转移。通过生成和覆盖一个令人感兴趣的面部区域,对面部进行广泛的实验,验证了拟议方法在保护脸部图像不受不理想的视觉检查、重建和识别方面的有效性,同时保持高任务可用性和性。结果显示,拟议方法在不受保护的ARCFASFA和SON格式之外实现了可比较的识别准确性和计算成本。