PrivacyPreserving Robot Vision with Anonymized Faces by Extreme Low Resolution As smart cameras are becoming ubiquitous in mobile robot systems, there is an increasing concern in camera devices invading peoplexe2x80x99s privacy by recording unwanted images. We want to fundamentally protect privacy by blurring unwanted blocks in images, such as faces, yet ensure that the robots can understand the video for their perception. In this paper, we propose a novel mobile robot framework with a deep learningbased privacypreserving camera system. The proposed camera system detects privacysensitive blocks, i.e., human face, from extreme low resolution (LR) images, and then dynamically enhances the resolution of only privacyinsensitive blocks, e.g., backgrounds. Keeping all the face blocks to be extreme LR of 15x15 pixels, we can guarantee that human faces are never at high resolution (HR) in any of processing or memory, thus yielding strong privacy protection even from cracking or backdoors. Our camera system produces an image on a realtime basis, the human faces of which are in extreme LR while the backgrounds are in HR. We experimentally confirm that our proposed face detection camera system outperforms the stateoftheart small face detection algorithm, while the robot performs ORBSLAM2 well even with videos of extreme LR faces. Therefore, with the proposed system, we do not too much sacrifice robot perception performance to protect privacy.

A Framework for Evaluating Image Obfuscation under Deep LearningAssisted Privacy Attacks Computer vision applications such as object detection and recognition, allow machines to visualize and perceive their environments. Nevertheless, these applications are guided by learningbased methods that require capturing, storing and processing large amounts of images thus rendering privacy and anonymity a major concern. In return, image obfuscation techniques (i.e., pixelating, blurring, and masking) have been developed to protect the sensitive information in images. In this paper, we propose a framework to evaluate and recommend the most robust obfuscation techniques in a specific domain of application. The proposed framework reconstructs obfuscated faces via deep learningassisted attacks and assesses the reconstructions using structuralidentitybased metrics. To evaluate and validate our approach, we conduct our experiments on a publicly available celebrity faces dataset. The obfuscation techniques considered are pixelating, blurring and masking. We evaluate the faces reconstructions against five deep learningassisted privacy attackers. The most resilient obfuscation technique is recommended with regard to structural and identitybased metrics.