Wenjun Zhu (Zhejiang University), Yuan Sun (Zhejiang University), Jiani Liu (Zhejiang University), Yushi Cheng (Zhejiang University), Xiaoyu Ji (Zhejiang University), Wenyuan Xu (Zhejiang University)

The proliferation of images captured from millions of cameras and the advancement of facial recognition (FR) technology have made the abuse of FR a severe privacy threat. Existing works typically rely on obfuscation, synthesis, or adversarial examples to modify faces in images to achieve anti-facial recognition (AFR). However, the unmodified images captured by camera modules that contain sensitive personally identifiable information (PII) could still be leaked. In this paper, we propose a novel approach, ***CamPro***, to capture inborn AFR images. ***CamPro*** enables well-packed commodity camera modules to produce images that contain little PII and yet still contain enough information to support other non-sensitive vision applications, such as person detection. Specifically, ***CamPro*** tunes the configuration setup inside the camera image signal processor (ISP), i.e., color correction matrix and gamma correction, to achieve AFR, and designs an image enhancer to keep the image quality for possible human viewers. We implemented and validated ***CamPro*** on a proof-of-concept camera, and our experiments demonstrate its effectiveness on ten state-of-the-art black-box FR models. The results show that ***CamPro*** images can significantly reduce face identification accuracy to 0.3% while having little impact on the targeted non-sensitive vision application. Furthermore, we find that ***CamPro*** is resilient to adaptive attackers who have re-trained their FR models using images generated by ***CamPro***, even with full knowledge of privacy-preserving ISP parameters.

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