Privacy Preserving Visual SLAM

Abstract

This study proposes a privacy-preserving Visual SLAM framework for estimating camera poses and performing bundle adjustment with mixed line and point clouds in real time. Previous studies have proposed localization methods to estimate a camera pose using a line-cloud map for a single image or a reconstructed point cloud. These methods offer a scene privacy protection against the inversion attacks by converting a point cloud to a line cloud, which reconstruct the scene images from the point cloud. However, they are not directly applicable to a video sequence because they do not address computational efficiency. This is a critical issue to solve for estimating camera poses and performing bundle adjustment with mixed line and point clouds in real time. Moreover, there has been no study on a method to optimize a line-cloud map of a server with a point cloud reconstructed from a client video because any observation points on the image coordinates are not available to prevent the inversion attacks, namely the reversibility of the 3D lines. The experimental results with synthetic and real data show that our Visual SLAM framework achieves the intended privacy-preserving formation and realtime performance using a line-cloud map.

Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors

“…Mitigations for Attacks on Sparse Local Features. For reverse engineering attacks on local features, one notable recent work [43,13,42] proposes using line-based features to obfuscate the precise location of keypoints in the scene to make the reconstruction difficult. The key idea is to lift every keypoint location to a line with a random direction, but passing through the original 2D [13] or 3D keypoints [43].…”

“…Since the feature location can be anywhere on a line, this alleviates privacy implications in the standard mapping and localization process. Shibuya et al [42] later extended this approach for SLAM. Similarly, Dusmanu et al [10] represent a keypoint location as an affine subspace passing through the original point, as well as augmenting the subspace with adversarial feature samples, which makes it more difficult for an adversary to recover original image content.…”