Region of Interest Loss for Anonymizing Learned Image Compression
- URL: http://arxiv.org/abs/2406.05726v1
- Date: Sun, 9 Jun 2024 10:36:06 GMT
- Title: Region of Interest Loss for Anonymizing Learned Image Compression
- Authors: Christoph Liebender, Ranulfo Bezerra, Kazunori Ohno, Satoshi Tadokoro,
- Abstract summary: We show how to achieve sufficient anonymization such that human faces become unrecognizable while persons are kept detectable.
This approach enables compression and anonymization in one step on the capture device, instead of transmitting sensitive, nonanonymized data over the network.
- Score: 3.0936354370614607
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: 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.
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