Digital media (e.g., photographs, video) can be easily created, edited, and shared. Tools for editing digital media are capable of doing so while also maintaining a high degree of photo-realism. While many types of edits to digital media are generally benign, others can also be applied for malicious purposes. State-of-the-art face editing tools and software can, for example, artificially make a person appear to be smiling at an inopportune time, or depict authority figures as frail and tired in order to discredit individuals. Given the increasing ease of editing digital media and the potential risks from misuse, a substantial amount of effort has gone into media forensics. To this end, we created a challenge dataset of edited facial images to assist the research community in developing novel approaches to address and classify the authenticity of digital media. Our dataset includes edits applied to controlled, portrait-style frontal face images and full-scene in-the-wild images that may include multiple (i.e., more than one) face per image. The goals of our dataset is to address the following challenge questions: (1) Can we determine the authenticity of a given image (edit detection)? (2) If an image has been edited, can we \textit{localize} the edit region? (3) If an image has been edited, can we deduce (classify) what edit type was performed? The majority of research in image forensics generally attempts to answer item (1), detection. To the best of our knowledge, there are no formal datasets specifically curated to evaluate items (2) and (3), localization and classification, respectively. Our hope is that our prepared evaluation protocol will assist researchers in improving the state-of-the-art in image forensics as they pertain to these challenges.
翻译:数字媒体(例如照片、视频)可以很容易创建、编辑和共享。编辑数字媒体的工具能够做到这一点,同时保持高度的摄影现实主义。虽然对数字媒体的许多类型的编辑一般是无害的,但其他也可以用于恶意目的。例如,最先进的面部编辑工具和软件可以人工使一个人在不理想的时段微笑,或者将权威人物描绘成疲软和疲倦的,以便诋毁个人。鉴于编辑数字媒体的难度越来越大以及滥用的潜在风险,大量的努力已经进入媒体的法证工作。为此,我们创建了一个经编辑的面部图像的挑战数据集,以协助研究界制定新颖的方法,处理和分类数字媒体的真实性。例如,最先进的面部编辑工具和软件可以使一个人在不理想的时段里微笑,这些图像可能包含多重(例如,比一个多)的对每个图像的评价。我们的数据集的目标是应对以下的挑战:(1) 我们能否确定一个图像的准确性,一个我们所完成的图像的图像的正确性, 在一个区域里头段里, 一个我们可以确定一个我们所完成的图像的图像的正确性。(3) 在一个我们所完成的图像的图像的图像的图像的图像的图像的正确性 。