Concealing the identity of faces in oblique images with adaptive hopping Gaussian mixtures

TL;DR

This paper introduces an adaptive, locally hopping privacy filter for face images in aerial photos, effectively concealing identities while resisting parameter estimation attacks, demonstrated through experiments with face recognition algorithms.

Contribution

It proposes a novel adaptive and locally hopping privacy filter that distorts face images based on resolution and resists attacks, improving privacy protection in MAV photography.

Findings

The filter effectively conceals identities in high-resolution images.

It reduces distortion compared to non-adaptive methods.

The approach shows resilience against parameter estimation attacks.

Abstract

Cameras mounted on Micro Aerial Vehicles (MAVs) are increasingly used for recreational photography. However, aerial photographs of public places often contain faces of bystanders thus leading to a perceived or actual violation of privacy. To address this issue, we propose to pseudo-randomly modify the appearance of face regions in the images using a privacy filter that prevents a human or a face recogniser from inferring the identities of people. The filter, which is applied only when the resolution is high enough for a face to be recognisable, adaptively distorts the face appearance as a function of its resolution. Moreover, the proposed filter locally changes its parameters to discourage attacks that use parameter estimation. The filter exploits both global adaptiveness to reduce distortion and local hopping of the parameters to make their estimation difficult for an attacker. In order to evaluate the efficiency of the proposed approach, we use a state-of-the-art face recognition algorithm and synthetically generated face data with 3D geometric image transformations that mimic faces captured from an MAV at different heights and pitch angles. Experimental results show that the proposed filter protects privacy while reducing distortion and exhibits resilience against attacks.