The Pixel Non-Uniformity noise (PNU noise, for short) is a characteristic noise of digital camera sensors that has been originally used a mean to perform Source Camera Identification (SCI), that is, to identify the digital camera that has been used to take an image under scrutiny. Actually, its usage has been extended to other relevant application domains, such as carrying out sensor identification in iris biometrics, to resolve the integrity of a biometric authentication system, in health monitoring systems based on bio-signal processing, and to prevent health fraud scams.

As a consequence of this popularity, several counter-forensics techniques have been proposed in the scientific literature to deceive this identification approach and, thus, putting at risk its effectiveness. The goal of this paper is to experiment with some of these counter-forensics techniques and to assess whether they are able or not to deceive the PNU-based identification process. We focus our attention on the particular case where a target image is modified so as to appear as been taken by a different device than the original one (i.e., spoofing). The results of our experiments show that this kind of spoofing is apparently successful in deceiving the PNU-based identification process. However, we also show that the spoofed images retain traces of their originating camera. By leveraging on this information, it is still possible to trace back the device used to take that image. This finding may have a deep impact on the usage of Pixel Non Uniformity (PNU) noise for identification tasks, such as the ones carried out by biometric recognition and monitoring systems, and on the possibility to really cheat them by means of counter-forensics techniques.

Pixel Non-Uniformity噪声（简称PNU噪声）是数码相机传感器的一种特征噪声，最初被用来作为一种手段来进行源相机识别（SCI），即识别已经使用过的数码相机。在仔细检查下拍摄图像。实际上，它的使用已经扩展到其他相关应用领域，例如在虹膜生物识别中进行传感器识别，解决生物识别认证系统的完整性，在基于生物信号处理的健康监测系统中，以及防止健康欺诈诈骗。 .

由于这种流行，科学文献中提出了几种反取证技术来欺骗这种识别方法，从而使其有效性处于危险之中。本文的目标是试验其中一些反取证技术，并评估它们是否能够欺骗基于 PNU 的识别过程。我们将注意力集中在目标图像被修改以使其看起来像是由与原始设备不同的设备拍摄的特定情况（即，欺骗）。我们的实验结果表明，这种欺骗在欺骗基于 PNU 的识别过程中显然是成功的。然而，我们还表明欺骗图像保留了其原始相机的痕迹。通过利用这些信息，仍然可以追溯到用于拍摄该图像的设备。这一发现可能对像素非均匀性 (PNU) 噪声在识别任务中的使用产生深远影响，例如生物识别和监控系统执行的任务，以及通过反取证技术真正欺骗它们的可能性.