论文标题
用于重新采样检测的多块聚合模型
multi-patch aggregation models for resampling detection
论文作者
论文摘要
如今，捕获的图像具有智能手机和数码单反相机的不同尺寸，允许用户从可用图像分辨率的列表中进行选择。因此，法医算法必须进行诸如重采样检测的范围，以便于尺寸良好地缩放。但是，在我们的实验中，我们观察到许多最先进的法医算法对图像大小敏感，尽管使用多个图像尺寸对其进行了重新训练，但在以不同维度进行操作时，它们的性能会迅速退化。为了解决这个问题，我们提出了一种新颖的合并策略，称为迭代合并。这种汇总策略可以在离散的情况下动态调整输入张量，而不会像ROI Max-Pooling一样多丢失信息。该合并策略可以与任何现有的深层模型一起使用，出于演示目的，我们在Resnet-18上显示了对重新采样检测的情况的实用性，用于对任何图像操纵图像进行的基本操作。与现有的策略和最大流动相比，公共数据集的提高了7-8％。
Images captured nowadays are of varying dimensions with smartphones and DSLR's allowing users to choose from a list of available image resolutions. It is therefore imperative for forensic algorithms such as resampling detection to scale well for images of varying dimensions. However, in our experiments, we observed that many state-of-the-art forensic algorithms are sensitive to image size and their performance quickly degenerates when operated on images of diverse dimensions despite re-training them using multiple image sizes. To handle this issue, we propose a novel pooling strategy called ITERATIVE POOLING. This pooling strategy can dynamically adjust input tensors in a discrete without much loss of information as in ROI Max-pooling. This pooling strategy can be used with any of the existing deep models and for demonstration purposes, we show its utility on Resnet-18 for the case of resampling detection a fundamental operation for any image sought of image manipulation. Compared to existing strategies and Max-pooling it gives up to 7-8% improvement on public datasets.
