A Theoretical Framework for Estimating False Acceptance Rate of PRNU-Based Camera Identification
A Theoretical Framework for Estimating False Acceptance Rate of PRNU-Based Camera Identification
复制标题
DOI：
10.1109/tifs.2017.2692683
复制
发表时间：
2017-04
影响因子：
6.8
通讯作者：
Shota Saito;Yoichi Tomioka;H. Kitazawa
中科院分区：
文献类型：
--
作者：
Shota Saito;Yoichi Tomioka;H. Kitazawa
近年来，摄像机识别方法在数字取证领域引起了广泛的关注。现有的摄像机识别方法使用诸如Exif报头数据和图像噪声之类的特征来指示摄像机的特征。其中，光响应非均匀(PRNU)噪声包含图像传感器的独特特征，并且对于每个相机而言是不同的。一种利用PRNU噪声的摄像机识别方法应具有较高的识别能力，先前提出了一种利用聚类PRNU噪声的成对幅度关系的摄像机识别方法。通常，识别精度是根据测试数据集估计的，例如德累斯顿图像数据库。然而，在传统的评估方法中，只能相对于数据库内的图像范围来评估识别精度。在实际应用中需要更详细的精度评价方法。此外，还没有研究报道一种能够保证较低FAR(例如，$Rm{Far}=10^{-9}$)的基于PRNU对的分簇相机识别的错误接受率(FAR)评估方法。本文提出了一种新的像素聚类方法，该方法利用聚类的PRNU噪声对保证摄像机识别的FAR，并基于数学模型的概率计算来评估其FAR。此外，我们通过使用Shapiro-Wilk检验来研究合适的团簇大小以进行FAR评估。结果表明，该评价方法能够可靠地计算基于聚类PRNU噪声对的摄像机识别方法的FAR。为了证明我们的计算的有效性，我们将实际识别结果与建议的计算结果进行了比较。在本例中，我们使用了来自德累斯顿图像数据库的16958个查询图像，这是一个基准数据集。我们的评估结果表明，即使在FAR$=10^-9}$的情况下，该识别方法对10个被测摄像头中的5(8)个保持了小于5%(10%)的错误拒绝率。
In recent years, camera identification methods have attracted attention in the field of digital forensics. The existing camera identification methods use features, such as the Exif header data and image noise, that indicate the characteristics of the camera. Of them, photo-response non-uniformity (PRNU) noise contains the unique features of an image sensor and is different for each individual camera. A camera identification method using the PRNU noise should have high identification ability, and a camera identification method using the pairwise magnitude relations of the clustered PRNU noise was previously proposed. In general, identification accuracy is estimated from test data sets, such as the Dresden image database. However, identification accuracy can be evaluated only with respect to the range of images within a database in the conventional evaluation method. A more detailed accuracy evaluation method is required for practical use. Furthermore, studies have not yet reported a false acceptance rate (FAR) evaluation method for the clustered PRNU pair-based camera identification capable of guaranteeing a low FAR (e.g., $\rm {FAR}=10^{-9}$ ). In this paper, we proposed a new pixel clustering method that guarantees An FAR for camera identification using pairs of clustered PRNU noise, and evaluate its FAR based on a probability calculation of a mathematical model. In addition, we investigate the appropriate cluster size by using the Shapiro–Wilk test for an FAR evaluation. We show that it is possible to reliably calculate the FAR of a clustered PRNU noise pair-based camera identification method by using the proposed evaluation method. To demonstrate the validity of our calculations, we compare the actual identification result with the result of the proposed calculation. In this case, we used 16 958 query images from the Dresden image database, which is a benchmark data set. The results of our evaluation indicate that this identification method maintains a false rejection rate of less than 5% (10%) for 5 (8) of the 10 tested cameras even for FAR $ = 10^{-9}$ .