Forensic analyses of digital images rely heavily on the traces of in-camera and out-camera processes left on the acquired images. Such traces represent a sort of camera fingerprint. If one is able to recover them, by suppressing the high-level scene content and other disturbances, a number of forensic tasks can be easily accomplished. A notable example is the PRNU pattern, which can be regarded as a device fingerprint, and has received great attention in multimedia forensics. In this paper, we propose a method to extract a camera model fingerprint, called noiseprint, where the scene content is largely suppressed and model-related artifacts are enhanced. This is obtained by means of a Siamese network, which is trained with pairs of image patches coming from the same (label +1) or different (label -1) cameras. Although the noiseprints can be used for a large variety of forensic tasks, in this paper we focus on image forgery localization. Experiments on several datasets widespread in the forensic community show noiseprint-based methods to provide state-of-the-art performance.

中文翻译：

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噪声指纹：基于 CNN 的相机模型指纹


数字图像的取证分析在很大程度上依赖于所采集图像上留下的相机内和相机外处理的痕迹。这些痕迹代表了一种相机指纹。如果能够通过抑制高级场景内容和其他干扰来恢复它们，则可以轻松完成许多取证任务。一个著名的例子是 PRNU 模式，它可以被视为设备指纹，并且在多媒体取证中受到了极大的关注。在本文中，我们提出了一种提取相机模型指纹的方法，称为噪声指纹，其中场景内容在很大程度上被抑制并增强了与模型相关的伪影。这是通过 Siamese 网络获得的，该网络使用来自相同（标签 +1）或不同（标签 -1）相机的图像块对进行训练。尽管噪声指纹可用于多种取证任务，但在本文中，我们重点关注图像伪造定位。对法医界广泛使用的几个数据集进行的实验表明，基于噪声指纹的方法可以提供最先进的性能。