论文标题
基于设备的图像匹配与相似性学习通过卷积神经网络的相似性学习
Device-based Image Matching with Similarity Learning by Convolutional Neural Networks that Exploit the Underlying Camera Sensor Pattern Noise
论文作者
论文摘要
数字图像取证中的具有挑战性的问题之一是能够识别由同一相机设备捕获的图像。这些知识可以通过分析数字图像来帮助法医专家收集有关嫌疑犯的情报。在本文中，我们提出了一个分为两部分的网络，以量化给定图像具有相同源相机的可能性，并在基准的德累斯顿数据集上对其进行了评估，该数据集包含来自31个不同摄像机的1851张图像。据我们所知，我们是第一个解决基于设备的图像匹配挑战的人。尽管拟议的方法尚未准备就绪，但我们的实验表明，这个方向值得追求，目前达到85％的准确性。这项正在进行的工作是欧盟资助的项目4NSEEK与针对儿童性虐待的取证有关的一部分。
One of the challenging problems in digital image forensics is the capability to identify images that are captured by the same camera device. This knowledge can help forensic experts in gathering intelligence about suspects by analyzing digital images. In this paper, we propose a two-part network to quantify the likelihood that a given pair of images have the same source camera, and we evaluated it on the benchmark Dresden data set containing 1851 images from 31 different cameras. To the best of our knowledge, we are the first ones addressing the challenge of device-based image matching. Though the proposed approach is not yet forensics ready, our experiments show that this direction is worth pursuing, achieving at this moment 85 percent accuracy. This ongoing work is part of the EU-funded project 4NSEEK concerned with forensics against child sexual abuse.
