Preprocessing Reference Sensor Pattern Noise via Spectrum Equalization
Preprocessing Reference Sensor Pattern Noise via Spectrum Equalization
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DOI：
10.1109/tifs.2015.2478748
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发表时间：
2016
影响因子：
6.8
通讯作者：
Xufeng Lin;Chang-Tsun Li
中科院分区：
文献类型：
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作者：
Xufeng Lin;Chang-Tsun Li
虽然传感器模式噪声(SPN)已被证明是唯一识别数码相机的一种有效手段，但相机之间共享的一些非唯一伪影经过相同或相似的相机内处理过程，往往会导致错误识别。因此，抑制这些不需要的伪影以提高精度和可靠性是必要和必要的。在本文中，我们提出了一种新的预处理方法，以减弱非唯一伪影对参考SPN的影响，从而降低误识率。具体地说，我们根据SPN的局部特征通过检测和抑制峰值来均衡参考SPN的幅值谱，以消除干扰的周期性伪影。结合6种SPN提取或增强方法，本文提出的谱均衡算法在德累斯顿图像库和我们自己的数据库上进行了评估，并与现有的预处理方案进行了比较。实验结果表明，在总体接收机工作特性曲线和从混淆矩阵计算的kappa统计量方面，该方法的性能优于或至少与现有方法相当，并且对中、小图像块具有更强的抗JPEG压缩能力。
Although sensor pattern noise (SPN) has been proved to be an effective means to uniquely identify digital cameras, some non-unique artifacts, shared among cameras undergo the same or similar in-camera processing procedures, often give rise to false identifications. Therefore, it is desirable and necessary to suppress these unwanted artifacts so as to improve the accuracy and reliability. In this paper, we propose a novel preprocessing approach for attenuating the influence of the non-unique artifacts on the reference SPN to reduce the false identification rate. Specifically, we equalize the magnitude spectrum of the reference SPN through detecting and suppressing the peaks according to the local characteristics, aiming at removing the interfering periodic artifacts. Combined with six SPN extractions or enhancement methods, our proposed spectrum equalization algorithm is evaluated on the Dresden image database as well as our own database, and compared with the state-of-the-art preprocessing schemes. The experimental results indicate that the proposed procedure outperforms, or at least performs comparable with, the existing methods in terms of the overall receiver operating characteristic curves and kappa statistic computed from a confusion matrix, and tends to be more resistant to JPEG compression for medium and small image blocks.