论文标题
SPN-CNN：通过深度学习提高基于传感器的源摄像机归因
SPN-CNN: Boosting Sensor-Based Source Camera Attribution With Deep Learning
论文作者
论文摘要
我们探讨了基于数据驱动框架中传感器噪声提高源相机标识的方法。我们的重点是在测试时从单个图像中提取传感器模式噪声（SPN）。如果现有作品抑制滋扰含量，而deo的过滤器在很大程度上对特定的SPN感兴趣的信号不可知，那么我们证明了一种深度学习方法可以产生更合适的提取器，从而导致改进的源归因。各种公共数据集上的一系列广泛实验证实了我们的方法的可行性及其对图像操作本地化和视频源归因的适用性。对潜在陷阱的批判性讨论完成了文本。
We explore means to advance source camera identification based on sensor noise in a data-driven framework. Our focus is on improving the sensor pattern noise (SPN) extraction from a single image at test time. Where existing works suppress nuisance content with denoising filters that are largely agnostic to the specific SPN signal of interest, we demonstrate that a~deep learning approach can yield a more suitable extractor that leads to improved source attribution. A series of extensive experiments on various public datasets confirms the feasibility of our approach and its applicability to image manipulation localization and video source attribution. A critical discussion of potential pitfalls completes the text.
