随着《通用数据保护条例》等隐私法规的重要性日益增加，对视觉数据进行匿名化在各机构中变得越来越相关。然而，匿名化可能会负面影响依赖视觉特征的计算机视觉系统，如基于内容的图像检索（CBIR）。尽管如此，匿名化对CBIR的影响尚未被系统研究。本研究填补了这一空白，受DOKIQ项目启发，该项目是用于文档验证的人工智能系统，已被巴登-符腾堡州刑事警察办公室积极使用。我们提出了一种简单的评估框架：匿名化后的检索结果应尽可能接近匿名化前的结果。为此，我们系统地评估匿名化的影响，使用两个公开数据集和内部DOKIQ数据集。我们的实验涵盖三种匿名化方法、四种匿名化程度和四种训练策略，均基于最先进的骨干网络Self-Distillation with No Labels（DINO）v2。我们的结果揭示了对原始数据训练的模型在匿名化后产生最相似检索结果的明显检索偏差。本文的发现为开发符合隐私要求的CBIR系统并保持性能提供了实用见解。
英文摘要：
With the growing importance of privacy regulations such as the General Data Protection Regulation, anonymizing visual data is becoming increasingly relevant across institutions. However, anonymization can negatively affect the performance of Computer Vision systems that rely on visual features, such as Content-Based Image Retrieval (CBIR). Despite this, the impact of anonymization on CBIR has not been systematically studied. This work addresses this gap, motivated by the DOKIQ project, an artificial intelligence-based system for document verification actively used by the State Criminal Police Office Baden-Württemberg. We propose a simple evaluation framework: retrieval results after anonymization should match those obtained before anonymization as closely as possible. To this end, we systematically assess the impact of anonymization using two public datasets and the internal DOKIQ dataset. Our experiments span three anonymization methods, four anonymization degrees, and four training strategies, all based on the state of the art backbone Self-Distillation with No Labels (DINO)v2. Our results reveal a pronounced retrieval bias in favor of models trained on original data, which produce the most similar retrievals after anonymization. The findings of this paper offer practical insights for developing privacy-compliant CBIR systems while preserving performance.
Benchmarking Time Series Generation Methods for Privacy-Preserving Forecasting
University of Minho（米尼奥大学）；Universidade do Porto（波尔图大学）；Laboratory for Artificial Intelligence and Computer Science (LIACC)（人工智能与计算机科学实验室（LIACC））；Fraunhofer Portugal AICOS（弗劳恩霍夫葡萄牙AICOS机构）；INESCTEC；University of Coimbra（科英布拉大学）
Telco-DPR: A Hybrid Dataset for Evaluating Retrieval Models of 3GPP Technical Specifications
Instituto de Telecomunicações（电信研究所）；CELFINET - A Cyient Company（CELFINET - 赛文特公司）；Instituto Superior de Engenharia Lisboa（里斯本高等工程学院）；Instituto Superior Técnico（里斯本高等技术学院）
Ev2R: Evaluating Evidence Retrieval in Automated Fact-Checking
Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系）；ETH AI Center（ETH人工智能中心）；School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦玛丽女王大学电子工程与计算机科学学院）；Department of Computer Science and Technology, University of Cambridge（剑桥大学计算机科学与技术系）
