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PANDORA: Preserving Privacy in PRNU-Based Source Camera Attribution

机译:Pandora:保留基于PRNU的源相机归属的隐私

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Photo Response Non-Uniformity (PRNU) noise-based source camera attribution is a popular digital forensic method. In this method, a camera fingerprint computed from a set of known images of the camera is matched against the extracted noise of an anonymous questionable image to find if the camera had taken the anonymous image. The possibility of privacy leak, however, is one of the main concerns of the PRNU-based method. Using the camera fingerprint (or the extracted noise), an adversary can identify the owner of the camera by matching the fingerprint with the noise of an image (or with the fingerprint computed from a set of images) crawled from a social media account In this paper, we address this privacy concern by encrypting both the fingerprint and the noise using the Boneh-Goh-Nissim (BGN) encryption scheme, and performing the matching In encrypted domain. To overcome leakage of privacy from the content of an image that is used in the fingerprint calculation, we compute the fingerprint within a trusted environment, such as ARM TrustZone. We present PANDORA that alms at minimizing privacy loss and allows authorized forensic experts to perform camera attribution.
机译:照片响应非均匀性(PRNU)基于噪声的源摄像机归属是一种流行的数字法医方法。在该方法中,从相机的一组已知图像计算的相机指纹与匿名可疑图像的提取噪声匹配,以查找相机是否已拍摄匿名图像。然而,隐私泄漏的可能性是基于PRNU的方法的主要问题之一。使用相机指纹(或提取的噪声),对手可以通过将指纹与图像的噪声(或从一组图像计算的指纹)匹配来识别相机的所有者通过从社交媒体帐户爬行纸张,我们通过使用BoneH-Goh-Nissim(BGN)加密方案来加密指纹和噪声来解决此隐私问题,并在加密域中执行匹配。为了从指纹计算中使用的图像的内容克服隐私的泄露,我们计算可信环境中的指纹,例如ARM TrustZone。我们在最大限度地降低隐私损失并允许授权的法医专家进行相机归因,提出潘多拉。

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