FITS: Matching Camera Fingerprints Subject to Software Noise Pollution
FITS: Matching Camera Fingerprints Subject to Software Noise Pollution
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DOI：
10.1145/3576915.3616600
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发表时间：
2023-11
期刊：
影响因子：
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通讯作者：
Liu Liu-Liu;Xinwen Fu;Xiaodong Chen;Jianpeng Wang;Zhongjie Ba;Feng Lin;Liwang Lu;Kui Ren
中科院分区：
文献类型：
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作者：
Liu Liu-Liu;Xinwen Fu;Xiaodong Chen;Jianpeng Wang;Zhongjie Ba;Feng Lin;Liwang Lu;Kui Ren
物理上不可克隆的硬件指纹可用于设备身份验证。光响应非均匀性（PRNU）是数码相机最可靠的硬件指纹，可以方便地从图像中提取。然而，我们发现图像后处理软件可能会将额外的噪声引入图像中。这种噪声的一部分仍然存在于提取的PRNU指纹中，并且很难通过传统的方法（例如去噪滤波器）来消除。我们将这种噪声定义为软件噪声，它会污染PRNU指纹并干扰对配备摄像头的设备进行身份验证。在本文中，我们提出了新的方法，指纹匹配，设备认证的关键步骤，在软件噪声的存在下。我们使用测试统计量，如峰值相关能量（PCE），以估计软件噪声相关性计算不同相机的PRNU指纹之间的互相关。在指纹匹配过程中，我们推导出两个PRNU指纹上的测试统计量与估计的软件噪声相关性的比值。我们将此比率表示为指纹与软件噪声比（FITS），这允许我们在用于指纹匹配的测试统计量中检测PRNU硬件噪声相关分量。通过90多部智能手机拍摄的10，000多张图像进行了广泛的实验来验证我们的方法，这些方法在污染指纹方面明显优于最先进的方法。本文首次研究了存在软件噪声的指纹匹配问题。
Physically unclonable hardware fingerprints can be used for device authentication. The photo-response non-uniformity (PRNU) is the most reliable hardware fingerprint of digital cameras and can be conveniently extracted from images. However, we find image post-processing software may introduce extra noise into images. Part of this noise remains in the extracted PRNU fingerprints and is hard to be eliminated by traditional approaches, such as denoising filters. We define this noise as software noise, which pollutes PRNU fingerprints and interferes with authenticating a camera armed device. In this paper, we propose novel approaches for fingerprint matching, a critical step in device authentication, in the presence of software noise. We calculate the cross correlation between PRNU fingerprints of different cameras using a test statistic such as the Peak to Correlation Energy (PCE) so as to estimate software noise correlation. During fingerprint matching, we derive the ratio of the test statistic on two PRNU fingerprints of interest over the estimated software noise correlation. We denote this ratio as the fingerprint to software noise ratio (FITS), which allows us to detect the PRNU hardware noise correlation component in the test statistic for fingerprint matching. Extensive experiments over 10,000 images taken by more than 90 smartphones are conducted to validate our approaches, which outperform the state-of-the-art approaches significantly for polluted fingerprints. We are the first to study fingerprint matching with the existence of software noise.