论文标题
相机模型和源设备特异性法医方法的融合，以改进篡改检测
Fusion of Camera Model and Source Device Specific Forensic Methods for Improved Tamper Detection
论文作者
论文摘要
基于PRNU的相机识别方法在图像法医文献中得到了广泛的研究。近年来，已经开发了基于CNN的相机模型识别方法。这两种方法还为篡改定位问题提供了解决方案。在本文中，我们提出了它们通过神经网络的组合，以实现更好的小规模篡改检测性能。根据结果，即使在高JPEG压缩下，融合方法的性能也比基础方法更好。对于小至100 $ \ times $ 100像素尺寸的伪造物，所提出的方法的表现优于最先进的方法，该方法验证了融合对小型图像伪造的定位的有用性。我们认为，使用基于PRNU的方法论，对于任何篡改检测管道都是可行的。
PRNU based camera recognition method is widely studied in the image forensic literature. In recent years, CNN based camera model recognition methods have been developed. These two methods also provide solutions to tamper localization problem. In this paper, we propose their combination via a Neural Network to achieve better small-scale tamper detection performance. According to the results, the fusion method performs better than underlying methods even under high JPEG compression. For forgeries as small as 100$\times$100 pixel size, the proposed method outperforms the state-of-the-art, which validates the usefulness of fusion for localization of small-size image forgeries. We believe the proposed approach is feasible for any tamper-detection pipeline using the PRNU based methodology.
