超越分类：隐私驱动的图像变换下的任务依赖可学习性
Beyond Classification: Task-Dependent Learnability under Privacy-Motivated Image Transformations
- Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB)（弗劳恩霍夫光学、系统技术与图像利用研究所）
- Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对现有PET评估仅依赖分类的局限，提出计算感知多任务协议，证明分类精度相近的PET在其他任务上表现差异显著，强调需开发更全面的PET评估协议。
AI中文摘要：
计算机视觉中的隐私增强技术（PETs）常依赖噪声或图像扰动来保护视觉数据，同时实现安全处理，这在任务性能与保护之间形成了权衡。这种权衡通常用图像分类来评估，分类主要捕捉语义可分性，即便几何、空间布局或局部边界发生显著改变仍具鲁棒性，因此作为通用视觉任务的代理过于简单。然而，详尽的下游任务评估计算成本高昂，因为需针对每种PET变换和参数设置单独训练模型。为此，我们提出一种用于模型训练中PET评估的计算感知多任务协议，它结合了针对视觉结构互补方面的轻量代理任务，且计算简单快速。针对不可逆隐私变换、基于密钥的块基元及可学习图像加密方案，我们证明，分类精度相近的PET在其他任务上表现差异显著。该结果凸显了需开发超越仅分类报告的PET评估协议。
英文摘要：
Privacy-Enhancing Technologies (PETs) in computer vision often rely on noise or image perturbations to protect visual data while securely processing it, creating a trade-off between task performance and protection. This trade-off is commonly evaluated using image classification, which primarily captures semantic separability and remains robust despite significant geometric, spatial layout or local boundary alterations. As a result, it is too simplistic as a proxy for generic vision tasks. Exhaustive downstream-task evaluation, however, is computationally expensive because models must often be trained for each PET transformation and parameter setting. We therefore propose a compute-aware multi-task protocol for evaluating PETs in model training. It combines lightweight proxy tasks that target complementary aspects of visual structure while remaining simple and fast to compute. Across irreversible privacy transformations, key-based block primitives, and learnable image encryption schemes, we demonstrate that PETs with similar classification accuracy can differ substantially on other tasks. The outcomes highlight the need for PET evaluation protocols that move beyond classification-only reporting.
