基于PRNU的源识别中的泄漏。质疑指纹的唯一性
A leak in PRNU based source identification. Questioning fingerprint uniqueness
摘要：
照片响应非均匀性（PRNU）被认为是图像源归因任务的最有效的跟踪。它的唯一性确保了从不同相机提取的传感器模式噪声是强不相关的，即使它们属于相同的相机模型。然而，随着计算摄影的出现，大多数最新的设备大量处理所获取的像素，可能会引入非唯一的伪影，这可能会降低PRNU噪声的独特性，特别是当分析中涉及相同设备模型的几个样本时。考虑到PRNU是一种图像取证技术，在全球范围内被执法机构广泛使用，因此必须在最新设备上不断验证此类技术。在本文中，我们对最近发布的45款智能手机和25款DSLR相机型号的33.000多张Flickr图像进行了广泛的测试，以确定该问题的普遍程度以及可能的原因。实验表明，大多数品牌，如三星，华为，佳能，尼康，富士，适马和徕卡，都受到这个问题的强烈影响。我们发现，高误报率的主要原因不能直接关系到特定的相机型号，固件，也不是图像内容。很明显，必须根据这些结果重新考虑基于\prnu的最新器械源识别的有效性。因此，本文件旨在呼吁科学界采取行动，而不是全面处理这一问题。此外，我们认为公布这些数据对于提高人们对执法界PRNU可靠性可能存在的问题的认识非常重要。
英文摘要：
Photo Response Non-Uniformity (PRNU) is considered the most effective trace for the image source attribution task. Its uniqueness ensures that the sensor pattern noises extracted from different cameras are strongly uncorrelated, even when they belong to the same camera model. However, with the advent of computational photography, most recent devices heavily process the acquired pixels, possibly introducing non-unique artifacts that may reduce PRNU noise's distinctiveness, especially when several exemplars of the same device model are involved in the analysis. Considering that PRNU is an image forensic technology that finds actual and wide use by law enforcement agencies worldwide, it is essential to keep validating such technology on recent devices as they appear. In this paper, we perform an extensive testing campaign on over 33.000 Flickr images belonging to 45 smartphone and 25 DSLR camera models released recently to determine how widespread the issue is and which is the plausible cause. Experiments highlight that most brands, like Samsung, Huawei, Canon, Nikon, Fujifilm, Sigma, and Leica, are strongly affected by this issue. We show that the primary cause of high false alarm rates cannot be directly related to specific camera models, firmware, nor image contents. It is evident that the effectiveness of \prnu based source identification on the most recent devices must be reconsidered in light of these results. Therefore, this paper is intended as a call to action for the scientific community rather than a complete treatment of the subject. Moreover, we believe publishing these data is important to raise awareness about a possible issue with PRNU reliability in the law enforcement world.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
