取证相机识别：非标称曝光的影响
Forensic Camera Identification: Effects of Off-Nominal Exposures
AI总结：
本文研究非标称曝光图像对PRNU相机识别错误率的影响，构建8400张图像数据集，发现欠曝光使假阳性率升至约两百分之一，过曝光使真阳性率降至82.90%，强调需持续研究以满足法庭取证标准。
摘要：
光响应非均匀性（PRNU）是一种能够将数字照片与其拍摄相机相匹配的技术。由于其在取证调查中的应用以及取证专家在法庭上的使用，确保该技术在各种证据图像类型下的错误率可靠至关重要。特别是，具有非标称曝光的图像并不少见。本文对具有不同曝光类型（过暗或过亮）的图像对PRNU源相机识别错误率的影响进行了初步研究。我们构建了一个由8400张精心采集的图像组成的新数据集，图像范围从欠曝光（过暗）到标称曝光再到过曝光（过亮）。我们首先仅使用标称曝光图像建立基线错误率，得到真阳性率为100%，真阴性率为99.92%。当测试非标称图像时，我们发现了显著的结果：欠曝光图像的真阴性率为99.46%（假阳性率约为两百分之一，在取证背景下通常不可接受），而过曝光图像的真阳性率降至82.90%。我们的结果凸显了继续研究PRNU源相机识别错误率的重要性，以确保符合法庭对取证证据可采性所设定的高标准。
英文摘要：
Photo response non-uniformity (PRNU) is a technology that can match a digital photograph to the camera that took it. Due to its use in forensic investigations and use by forensic experts in court, it is important that error rates for this technology are reliable for a wide range of evidence image types. In particular, images with off-nominal exposures are not uncommon. This paper presents a preliminary investigation of the impact that images with different exposure types - too dark or too light - have on error rates for PRNU source camera identification. We construct a new dataset comprised of 8400 carefully collected images ranging from under-exposed (too dark) to nominally exposed to over-exposed (too bright). We first establish baseline error rates using only nominally exposed images, resulting in a true-positive rate of 100% and a true-negative rate of 99.92%. When off-nominal images are tested, we find striking results: the true-negative rate for under-exposed images is 99.46% (a false-positive rate of roughly one in two hundred, typically unacceptable in a forensic context), and for over-exposed images the true-positive rate falls to 82.90%. Our results highlight the importance of continued study of error rates for the PRNU source camera identification to assure adherence to the high standards set for admissibility of forensic evidence in court.
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