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A compact representation of sensor fingerprint for camera identification and fingerprint matching

机译:用于相机识别和指纹匹配的传感器指纹的紧凑表示

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摘要

Sensor Pattern Noise (SPN) has been proved as an effective fingerprint of imaging devices to link pictures to the cameras that acquired them. In practice, forensic investigators usually extract this camera fingerprint from large image block to improve the matching accuracy because large image blocks tend to contain more SPN information. As a result, camera fingerprints usually have a very high dimensionality. However, the high dimensionality of fingerprint will incur a costly computation in the matching phase, thus hindering many interesting applications which require an efficient real-time camera matching. To solve this problem, an effective feature extraction method based on PCA and LDA is proposed in this work to compress the dimensionality of camera fingerprint. Our experimental results show that the proposed feature extraction algorithm could greatly reduce the size of fingerprint and enhance the performance in term of Receiver Operating Characteristic (ROC) curve of several existing methods.\ud
机译:传感器图案噪声(SPN)已被证明是将图像链接到获取图像的相机的有效成像设备指纹。在实践中,法医调查人员通常会从大图像块中提取此相机指纹,以提高匹配精度,因为大图像块往往包含更多SPN信息。结果,照相机指纹通常具有非常高的尺寸。但是,指纹的高维数将导致在匹配阶段进行昂贵的计算,从而阻碍了许多需要高效实时相机匹配的有趣应用。针对这一问题,本文提出了一种基于PCA和LDA的有效特征提取方法来压缩相机指纹的维数。我们的实验结果表明,从几种现有方法的接收器工作特性(ROC)曲线来看,所提出的特征提取算法可以大大减小指纹的大小并提高性能。

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