Revealing Unintentional Information Leakage in Low-Dimensional Facial Portrait Representations

• URL: http://arxiv.org/abs/2503.09306v1
• Date: Wed, 12 Mar 2025 12:01:15 GMT
• Title: Revealing Unintentional Information Leakage in Low-Dimensional Facial Portrait Representations
• Authors: Kathleen Anderson, Thomas Martinetz,
• Abstract summary: We evaluate the information that can unintentionally leak into the low dimensional output of a neural network.<n>We reconstruct an input image from a 40- or 32-element feature vector that intends to only describe abstract attributes of a facial portrait.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We evaluate the information that can unintentionally leak into the low dimensional output of a neural network, by reconstructing an input image from a 40- or 32-element feature vector that intends to only describe abstract attributes of a facial portrait. The reconstruction uses blackbox-access to the image encoder which generates the feature vector. Other than previous work, we leverage recent knowledge about image generation and facial similarity, implementing a method that outperforms the current state-of-the-art. Our strategy uses a pretrained StyleGAN and a new loss function that compares the perceptual similarity of portraits by mapping them into the latent space of a FaceNet embedding. Additionally, we present a new technique that fuses the output of an ensemble, to deliberately generate specific aspects of the recreated image.

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