Privacy preservation network with global-aware focal loss for Interactive Personal Visual Privacy Preservation

Abstract

Existing visual privacy preservation methods often encounter the challenge of over-protection, which usually fails to accurately protect a specified person in an image. This paper introduces a novel task called “Interactive Personal Visual Privacy Preservation (IPVPP)”, with the objective of safeguarding a desired individual in an image based on user-click prompts. We propose a new framework, Privacy Preservation Network (PPNet) with Global-aware Focal Loss, tailored for IPVPP task. To address the sample imbalance issue in IPVPP, we present a Global-aware Focal Loss (GFL), which introduces global and local difficulty balance weights to dynamically adjust the overall decision boundary, thereby mitigating the sample imbalance problem for better optimization of models. Furthermore, to tackle the insufficient fusion between click and visual features, we propose a Click-Image Fusion (CIF) module, which effectively emphasizes the interaction between click and image features, providing robust query inputs to the model. We finally employ generative adversarial networks to ensure the reusability of privacy images, striking a balance between privacy preservation and image value. Experimental results on the DAVIS dataset demonstrate the effectiveness of PPNet, achieving improvements measured in NoC 90, IS, SSIM, F-1, and DIR, with 0.22, 0.19, 0.009, 4.23, and 2.15 improvements, respectively.