  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.
