Obfuscation Based Privacy Preserving Representations are Recoverable Using Neighborhood Information (2409.11536v2)

Abstract: Rapid growth in the popularity of AR/VR/MR applications and cloud-based visual localization systems has given rise to an increased focus on the privacy of user content in the localization process. This privacy concern has been further escalated by the ability of deep neural networks to recover detailed images of a scene from a sparse set of 3D or 2D points and their descriptors - the so-called inversion attacks. Research on privacy-preserving localization has therefore focused on preventing these inversion attacks on both the query image keypoints and the 3D points of the scene map. To this end, several geometry obfuscation techniques that lift points to higher-dimensional spaces, i.e., lines or planes, or that swap coordinates between points % have been proposed. In this paper, we point to a common weakness of these obfuscations that allows to recover approximations of the original point positions under the assumption of known neighborhoods. We further show that these neighborhoods can be computed by learning to identify descriptors that co-occur in neighborhoods. Extensive experiments show that our approach for point recovery is practically applicable to all existing geometric obfuscation schemes. Our results show that these schemes should not be considered privacy-preserving, even though they are claimed to be privacy-preserving. Code will be available at https://github.com/kunalchelani/RecoverPointsNeighborhood.

• The paper introduces a novel framework that uses neighborhood information to accurately recover obfuscated points.
• The learning-based approach predicts neighborhoods from descriptors, validating its effectiveness across various obfuscation techniques.
• Extensive experiments using geometric and perceptual metrics reveal vulnerabilities in current privacy-preserving obfuscation methods.

Obfuscation-Based Privacy-Preserving Representations are Recoverable Using Neighborhood Information

Introduction

The paper "Obfuscation Based Privacy Preserving Representations are Recoverable Using Neighborhood Information" addresses a critical issue in visual localization and related applications where privacy concerns are paramount. The authors contend with the challenges posed by inversion attacks, where deep neural networks recover original images from a set of 3D or 2D points and their descriptors. The studied obfuscation techniques are intended to protect user data, but the research demonstrates vulnerabilities in these methods.

Key Contributions

1. Framework for Point Recovery: The paper introduces a novel framework to approximate the positions of original points from obfuscated representations. The framework leverages neighborhood information to compute a recovery mapping.
2. Learning-Based Approach: The authors propose a learning-based approach to estimate neighborhoods of obfuscated points using descriptors preserved through obfuscation.
3. Comprehensive Evaluation: The proposed method is validated through extensive experiments that confirm its general applicability to a variety of existing geometry obfuscation techniques.

Methodology

Geometry Obfuscation

A broad definition of geometry obfuscation is provided, encapsulating all previous methods. The methods map points to higher-dimensional representations like lines or planes in ways that theoretically prevent direct recovery.

Recovering Points Using Neighborhood Information

The proposed recovery mapping ($\mathcal{R}$) is computed by minimizing the distances between each point and its neighbors, utilizing known neighborhood sets. This generic method is applicable to different obfuscation schemes, including:

• Points lifted to lines.
• Points lifted to planes.
• Coordinate permutation methods.

The robustness of this approach is tested against imperfect neighborhoods, incorporating a RANSAC-like loop to filter outliers and improve accuracy.

Estimating Neighborhoods From Descriptors

The neighborhood estimation is poised as a machine learning problem where a neural network, taking descriptors as input, learns to predict the similarity matrix that represents neighborhood proximity. This is crucial for practical scenarios where ground truth neighbor information is not available.

Experimental Evaluation

Geometric and Perceptual Metrics

The geometric accuracy of the method is benchmarked across different datasets (e.g., 7-scenes, Cambridge) and for different types of keypoints (e.g., SIFT, SuperPoint). The results reveal that:

• Line-based obfuscations: Susceptible to recovery even with lower inlier ratios in the neighborhood information.
• Plane and coordinate permutation obfuscations: More robust but can still be effectively compromised with sufficient neighbor information.

In terms of perceptual metrics, the reconstructed images from recovered points exhibit strong similarity to the original images, confirming the efficacy of the recovery method.

Learning-Based Neighborhood Estimation

The feasibility of using learned neighborhoods for point recovery is demonstrated, with a neural network effectively predicting neighborhoods and enabling the proposed recovery method. This emphasizes the insufficiency of geometric obfuscations alone in ensuring privacy.

Implications and Future Directions

Practical Implications

The findings indicate that all evaluated geometry obfuscation techniques, while claimed to be privacy-preserving, possess inherent vulnerabilities. This has significant ramifications for privacy in applications like AR/VR, SLAM, and cloud-based visual localization, urging the need for more robust privacy-preserving methods.

Theoretical Implications

The paper underscores the necessity of proving theoretical guarantees in privacy-preserving methods, pushing future research to define clear conditions under which privacy can be assured. Fusion of geometric and descriptor-based obfuscations may present more comprehensive solutions.

Conclusion

The paper delivers substantial evidence that current geometric obfuscation techniques are susceptible to recovery when leveraging neighborhood information. Through the proposed framework and learning-based approach, it makes a compelling case for reconsidering the privacy guarantees of obfuscation methods in visual localization systems. The insights gained pave the way for future advancements in developing truly privacy-preserving techniques.