论文解读《Image Splicing Detection and Localisation using EfficientNet and Modified U-Net Architecture》

论文解读《Image Splicing Detection and Localisation using EfficientNet and Modified U-Net Architecture》

平平无奇的论文，把我想到的idea的一部分给做了。

Abstract

• We propose a transfer learning (EfficientNet) based image classification algorithm for the classification of forged Images and EfficientNet
  based U-Net Architecture for localization. In the classification part, we extract the image patches from the forged regions and non-forged regions in an image and classify them. The classification fetched us state of the art result. In the localization part, we have built a U-Net architecture by replacing the U-Net Encoder part with EfficientNet layers and keeping the decoder part as usual. We then pass the forged images through the network to train the model. The localization experiment also fetched state-of-the-art results.

  我们提出了一种基于迁移学习 (EfficientNet) 的图像分类算法，用于对伪造图像进行分类，并提出了一种基于 EfficientNet 的 U-Net 架构进行定位。在分类部分，我们从图像中的伪造区域和非伪造区域中提取图像块并对其进行分类。分类为我们提供了最先进的结果。在定位部分，我们通过将 U-Net 编码器部分替换为 EfficientNet 层并保持解码器部分与往常一样，构建了一个 U-Net 架构。然后我们通过网络传递伪造的图像来训练模型。本地化实验也取得了最先进的结果。

II. PROPOSED METHODS

A. Proposed Splicing Detection Model

B. Proposed Splicing Localisation Model

III. EXPERIMENTAL RESULTS