Kunal Chelani, Signalbehandling och medicinsk teknik

Visual localization is a very important piece in the puzzle for many computer vision applications. It is essential for seamlessly running applications that rely on knowing the pose of the camera with respect to the 3D surroundings. Many such applications run on user devices such as smartphones, VR headsets, \etc, that have limited storage and compute. In such cases, it is practically more feasible to off-load the computations to a powerful cloud-based server. As visual information is stored on a remote server, or sent to it for obtaining the camera pose, it becomes important to ensure that private user content is not accessed in an unauthorized manner.
This thesis contributes towards increased protection of cloud-based visual localization systems against threats to user privacy. One of the popular ways to represent scenes and images for localization is through a sparse set of 3D or 2D points respectively. However, the point-based representation can reveal highly detailed images of the user scene, prompting research in obfuscating the geometry of these points. Papers A and B of this thesis highlight an important vulnerability of such geometry obfuscation methods that claim to preserve user privacy while enabling visual localization. This urges future methods to include clear guarantees about their claims of privacy preservation.
Paper C introduces a novel attack vector in a scenario where an adversary gains access to query the localization server of another user's scene with its own set of images. We show that an attacker can gain unauthorized information about presence and positions of objects in a user's 3D space. Based on the insight that recovering details from a very sparse geometric signal is difficult, we explore representing a scene in the form of only its outline. Paper D presents an efficient and accurate method to reconstruct the edges of a scene from images.