PRNU-based image processing is a key asset in digital multimedia forensics. It allows for reliable device identification and effective detection and localization of image forgeries, in very general conditions. However, performance impairs significantly in challenging conditions involving low quality and quantity of data. These include working on compressed and cropped images or estimating the camera PRNU pattern based on only a few images. To boost the performance of PRNU-based analyses in such conditions, we propose to leverage the image noiseprint, a recently proposed camera-model fingerprint that has proved effective for several forensic tasks. Numerical experiments on datasets widely used for source identification prove that the proposed method ensures a significant performance improvement in a wide range of challenging situations.

中文翻译：

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结合PRNU和噪声图以实现可靠而有效的设备源识别

基于PRNU的图像处理是数字多媒体取证中的关键资产。在非常普通的条件下，它可以实现可靠的设备识别以及图像伪造的有效检测和定位。但是，在涉及数据质量和数量较低的挑战性条件下，性能会大大受损。这些包括处理压缩和裁剪的图像或仅基于少量图像估计相机的PRNU模式。为了在这种情况下提高基于PRNU的分析的性能，我们建议利用图像噪声指纹，该图像噪声指纹是最近提出的一种相机模型指纹，已被证明对某些取证任务有效。在广泛用于源识别的数据集上的数值实验证明，该方法可确保在各种挑战性情况下的显着性能改进。