Most non-professional photo manipulations are not made using propriety software like Adobe Photoshop, which is expensive and complicated to use for the average consumer selfie-taker or meme-maker. Instead, these individuals opt for user friendly mobile applications like FaceTune and Pixlr to make human face edits and alterations. Unfortunately, there is no existing dataset to train a model to classify these type of manipulations. In this paper, we present a generative model that approximates the distribution of human face edits and a method for detecting Facetune and Pixlr manipulations to human faces.
翻译:多数非专业照片操作不是使用像Adobe Photoshop这样的适当软件,该软件对于普通消费者自制自制或meme-maker来说成本昂贵且复杂,而这些人选择了FaceTune和Pixlr等方便用户的移动应用程序来编辑和修改人的脸部。不幸的是,没有现有的数据集来训练这种类型的操纵的分类模型。在本文中,我们提出了一个与人脸部编辑的分布相近的基因化模型,以及一种将Facetune和皮克斯勒的操纵方法探测到人的脸部。