Abstract
Photographs taken in 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 identity 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 the values of its parameters to counter attacks that attempt to estimate them. The filter exploits both global adaptiveness to reduce distortion and local parameter hopping to make their estimation difficult for an attacker. In order to evaluate the efficiency of the proposed approach, we consider an important scenario of oblique face images: photographs taken with low altitude Micro Aerial Vehicles (MAVs). 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 is also robust against attacks.
Highlights
Photography in public places raises privacy concerns as bystanders who happen to be within the field of view of a camera are captured as well
Hardware based filters prevent the camera from taking images for example by bursting back an intense light for flash photography [19] or by detecting human face/body using an infrared sensor and obfuscating using a spatial light modulator sensor placed in front of the Charge Coupled Device (CCD) sensor [20], [21]
EXPERIMENTAL SET UP We compare Adaptive Hopping Gaussian Mixture Model (AHGMM) against Space Variant Gaussian Blur (SVGB) [17], Adaptive Gaussian Blur (AGB) [15] and Fixed Gaussian Blur (FGB), which uses a constant Gaussian kernel defined with respect to the highest resolution face
Summary
Photography in public places raises privacy concerns as bystanders who happen to be within the field of view of a camera are captured as well. Adaptive Gaussian Blur adaptively configures the Gaussian kernel depending upon the face resolution in order to minimise distortion, while Unmanned Aircraft Systems-Visual Privacy Guard blurs faces with a fixed filter. These methods are prone to parrot attacks [4] on the Gaussian blur. The main contributions of this paper are: (1) the idea of using Gaussian hopping kernels for privacy and utility preservation, (2) the generation of a large-scale synthetic face image data set emulating faces captured from an MAV, and (3) extensive experiments to validate the proposed Gaussian hopping kernels, including reconstruction attacks.
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