Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception?
Xiaoxiao Sun†‡ , Nidham Gazagnadou‡ , Vivek Sharma‡ ,
Lingjuan Lyu‡ , Hongdong Li†, Liang Zheng†
† Australian National University ‡ SonyAI
MOTIVATION: Inconsistency between existing metrics and human judgements on privacy information leakage. For each original image, we present two reconstructions produced by InvGrad. Below the reconstructed images, each colored √ corresponds to a different metric, indicating that the corresponding metric evaluates the reconstruction to have more information leakage. In A, according to PSNR, MSE, SSIM and LPIPS, the first reconstructed image is evaluated to have more privacy leakage than the second one (i.e., the first one has a higher PSNR, SSIM values, and a lower MSE and LPIPS values). However, human annotators perceive the first image as having less privacy leakage, since they cannot recognise this recognition (in contrast to the second reconstruction, which is recognizable and suggested to have more information leakage). Such inconsistency in privacy assessment is our key observation and motivation. Moreover, we observe in B that even these metrics themselves often disagree with each other.
Abstract
Hand-crafted image quality metrics, such as PSNR and SSIM, are commonly used to evaluate model privacy risk under reconstruction attacks. Under these metrics, reconstructed images that are determined to resemble the original one generally indicate more privacy leakage. Images determined as overall dissimilar, on the other hand, indicate higher robustness against attack. However, there is no guarantee that these metrics well reflect human opinions, which, as a judgement for model privacy leakage, are more trustworthy. In this paper, we comprehensively study the faithfulness of these hand-crafted metrics to human perception of privacy information from the reconstructed images. On 5 datasets ranging from natural images, faces, to fine-grained classes, we use 4 existing attack methods to reconstruct images from many different classification models and, for each reconstructed image, we ask multiple human annotators to assess whether this image is recognizable. Our studies reveal that the hand-crafted metrics only have a weak correlation with the human evaluation of privacy leakage and that even these metrics themselves often contradict each other. These observations suggest risks of current metrics in the community. To address this potential risk, we propose a learning-based measure called SemSim to evaluate the Semantic Similarity between the original and reconstructed images. SemSim is trained with a standard triplet loss, using an original image as an anchor, one of its recognizable reconstructed images as a positive sample, and an unrecognizable one as a negative. By training on human annotations, SemSim exhibits a greater reflection of privacy leakage on the semantic level. We show that SemSim has a significantly higher correlation with human judgment compared with existing metrics. Moreover, this strong correlation generalizes to unseen datasets, models and attack methods. We envision this work as a milestone for image quality evaluation closer to the human level.