  The use of AI in public spaces continually raises concerns about privacy and
the protection of sensitive data. An example is the deployment of detection and
recognition methods on humans, where images are provided by surveillance
cameras. This results in the acquisition of great amounts of sensitive data,
since the capture and transmission of images taken by such cameras happens
unaltered, for them to be received by a server on the network. However, many
applications do not explicitly require the identity of a given person in a
scene; An anonymized representation containing information of the person's
position while preserving the context of them in the scene suffices. We show
how using a customized loss function on region of interests (ROI) can achieve
sufficient anonymization such that human faces become unrecognizable while
persons are kept detectable, by training an end-to-end optimized autoencoder
for learned image compression that utilizes the flexibility of the learned
analysis and reconstruction transforms for the task of mutating parts of the
compression result. This approach enables compression and anonymization in one
step on the capture device, instead of transmitting sensitive, nonanonymized
data over the network. Additionally, we evaluate how this anonymization impacts
the average precision of pre-trained foundation models on detecting faces
(MTCNN) and humans (YOLOv8) in comparison to non-ANN based methods, while
considering compression rate and latency.
