论文标题
使用SOTA图像分类的锻造图像检测深度学习方法用于图像取证和错误级别分析
Forged Image Detection using SOTA Image Classification Deep Learning Methods for Image Forensics with Error Level Analysis
论文作者
论文摘要
使用深度学习机制带来了计算机视觉领域的进步。图像取证是计算机视觉应用的主要领域之一。图像的伪造是图像取证的子类别，可以使用错误级别分析检测到。使用此类图像作为输入，这可能是一个二进制分类问题，可以使用卷积神经网络的变化来利用它。在本文中，我们使用最先进的图像分类模型在错误级别分析引起的CASIA ITDE v.2数据集上进行转移学习。所使用的算法包括VGG-19，Inception-V3，Resnet-152-V2，XceptionNet和EfficityNet-V2L及其各自的方法和结果。
The advancement in the area of computer vision has been brought using deep learning mechanisms. Image Forensics is one of the major areas of computer vision application. Forgery of images is sub-category of image forensics and can be detected using Error Level Analysis. Using such images as an input, this can turn out to be a binary classification problem which can be leveraged using variations of convolutional neural networks. In this paper we perform transfer learning with state-of-the-art image classification models over error level analysis induced CASIA ITDE v.2 dataset. The algorithms used are VGG-19, Inception-V3, ResNet-152-V2, XceptionNet and EfficientNet-V2L with their respective methodologies and results.
