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Privacy Leakage of SIFT Features via Deep Generative Model Based Image Reconstruction
IEEE Transactions on Information Forensics and Security ( IF 6.8 ) Pub Date : 2021-04-01 , DOI: 10.1109/tifs.2021.3070427
Haiwei Wu , Jiantao Zhou

Many practical applications, e.g., content based image retrieval and object recognition, heavily rely on the local features extracted from the query image. As these local features are usually exposed to untrustworthy parties, the privacy leakage problem of image local features has received increasing attention in recent years. In this work, we thoroughly evaluate the privacy leakage of Scale Invariant Feature Transform (SIFT), which is one of the most widely-used image local features. We first consider the case that the adversary can fully access the SIFT features, i.e., both the SIFT descriptors and the coordinates are available. We propose a novel end-to-end, coarse-to-fine deep generative model for reconstructing the latent image from its SIFT features. The designed deep generative model consists of two networks, where the first one attempts to learn the structural information of the latent image by transforming from SIFT features to Local Binary Pattern (LBP) features, while the second one aims to reconstruct the pixel values guided by the learned LBP. Compared with the state-of-the-art algorithms, the proposed deep generative model produces much improved reconstructed results over three public datasets. Furthermore, we address more challenging cases that only partial SIFT features (either SIFT descriptors or coordinates) are accessible to the adversary. It is shown that, if the adversary can only have access to the SIFT descriptors while not their coordinates, then the modest success of reconstructing the latent image might be achieved for highly-structured images (e.g., faces) and probably would fail in general settings. In addition, the latent image usually can be reconstructed with acceptable quality solely from the SIFT coordinates. Our results would suggest that the privacy leakage problem can be avoided to a certain extent if the SIFT coordinates can be well protected.

中文翻译:

通过基于深度生成模型的图像重建实现SIFT功能的隐私泄漏

许多实际应用,例如基于内容的图像检索和对象识别,都严重依赖于从查询图像中提取的局部特征。由于这些局部特征通常暴露于不信任方,因此图像局部特征的隐私泄漏问题近年来受到越来越多的关注。在这项工作中,我们彻底评估尺度不变特征变换(SIFT)的隐私泄漏,SIFT是使用最广泛的图像局部特征之一。我们首先考虑敌方可以完全访问SIFT功能的情况,即SIFT描述符和坐标均可用。我们提出了一种新的端到端,从粗到细的深度生成模型,用于从SIFT特征中重建潜像。设计的深度生成模型由两个网络组成,其中第一个尝试通过将SIFT特征转换为局部二值模式(LBP)特征来学习潜像的结构信息,而第二个尝试重建由学习的LBP引导的像素值。与最新算法相比,所提出的深度生成模型在三个公共数据集上产生了改进的重构结果。此外,我们处理更具挑战性的情况,即对手只能访问部分SIFT功能(SIFT描述符或坐标)。结果表明,如果对手只能访问SIFT描述符,而不能访问其坐标,则对于高度结构化的图像(例如人脸),可能会成功地重建潜像,并且在一般情况下可能会失败。此外,通常仅从SIFT坐标就可以以可接受的质量重建潜像。我们的结果表明,如果可以很好地保护SIFT坐标,则可以在一定程度上避免隐私泄露问题。
更新日期:2021-04-20
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