17 Computer Vision News Computer Vision News this method, it is prone to reconstruction attacks, where protected information (such as gender) can be extracted from modified images. The second approach, which formed the core of the work that earned him the EAB award, was ASPECD: Adaptable SoftBiometric Privacy-Enhancement using Centroid Decoding for Face Verification. This contribution focuses on template-level manipulation. “You have an image, you extract the features, and then we have multiple modules that try to preserve the privacy of each attribute separately,” he explains. “If users have different privacy-related preferences, let’s say someone is only concerned about ethnicity, you can only apply this module. After it’s applied, it doesn’t preserve gender-related information. It just targets one or both. It depends on what the user wants. In each case, you would get a template comparable to all other preferences.” The first contribution of my PhD dissertation is PrivacyProber (the paper was published in IEEE Transactions on Dependable and Secure Computing). In this work we showed that existing SB-PETs, which manipulate raw image pixels, are to a high degree prone reconstruction attacks. Original image is denoted as I_or, and we produce its privacy-enhanced version I_pr (note that privacyenhancement flips gender prediction) using chosen privacyenhancing mechanism \psi. By transforming I_pr, we were able to obtain concealed soft-biometric information back, demonstrating the vulnerability of contemporary SB-PETs. Privacy-preserving face analytics …
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