Source identification is an important topic in image forensics, since it allows to trace back the origin of an image. This represents a precious information to claim intellectual property but also to reveal the authors of illicit materials. In this letter we address the problem of device identification based on sensor noise and propose a fast and accurate solution using convolutional neural networks (CNNs). Specifically, we propose a 2-channel-based CNN that learns a way of comparing camera fingerprint and image noise at patch level. The proposed solution turns out to be much faster than the conventional approach and to ensure an increased accuracy. This makes the approach particularly suitable in scenarios where large databases of images are analyzed, like over social networks. In this vein, since images uploaded on social media usually undergo at least two compression stages, we include investigations on double JPEG compressed images, always reporting higher accuracy than standard approaches.

中文翻译：

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基于CNN的快速源设备识别

来源识别是图像取证中的一个重要主题，因为它允许追溯图像的来源。这代表了一项宝贵的信息，既可以要求知识产权，也可以揭露非法材料的作者。在这封信中，我们解决了基于传感器噪声的设备识别问题，并使用卷积神经网络 (CNN) 提出了一种快速准确的解决方案。具体来说，我们提出了一种基于 2 通道的 CNN，它可以学习一种在补丁级别比较相机指纹和图像噪声的方法。结果表明，所提出的解决方案比传统方法快得多，并确保提高准确性。这使得该方法特别适用于分析大型图像数据库的场景，例如通过社交网络。在这种情况下，