论文标题
将PRNU和NOISEPRINT相结合，以进行鲁棒和高效的设备源标识
Combining PRNU and noiseprint for robust and efficient device source identification
论文作者
论文摘要
基于PRNU的图像处理是数字多媒体取证中的关键资产。它允许在非常一般的条件下进行可靠的设备识别以及图像伪造的有效检测和定位。但是，在涉及低质量和数据数量的具有挑战性的条件下，性能会严重损害。其中包括在压缩和裁剪的图像上工作，或仅根据几个图像来估算相机PRNU图案。为了在这种情况下提高基于PRNU的分析的性能，我们建议利用图像Noiseprint，这是一种最近提出的摄像机模型指纹，事实证明对几项法医任务有效。广泛用于源识别的数据集上的数值实验证明，所提出的方法可确保在各种具有挑战性的情况下的性能得到显着改善。
PRNU-based image processing is a key asset in digital multimedia forensics. It allows for reliable device identification and effective detection and localization of image forgeries, in very general conditions. However, performance impairs significantly in challenging conditions involving low quality and quantity of data. These include working on compressed and cropped images, or estimating the camera PRNU pattern based on only a few images. To boost the performance of PRNU-based analyses in such conditions we propose to leverage the image noiseprint, a recently proposed camera-model fingerprint that has proved effective for several forensic tasks. Numerical experiments on datasets widely used for source identification prove that the proposed method ensures a significant performance improvement in a wide range of challenging situations.
