In this paper, we present the Multi-Forgery Detection Challenge held
concurrently with the IEEE Computer Society Workshop on Biometrics at CVPR
2022. Our Multi-Forgery Detection Challenge aims to detect automatic image
manipulations including but not limited to image editing, image synthesis,
image generation, image photoshop, etc. Our challenge has attracted 674 teams
from all over the world, with about 2000 valid result submission counts. We
invited the Top 10 teams to present their solutions to the challenge, from
which three teams are awarded prizes in the grand finale. In this paper, we
present the solutions from the Top 3 teams, in order to boost the research work
in the field of image forgery detection.

本文介绍了同时与 2022 年 CVPR 上的 IEEE 计算机学会生物特征学车间举办的 Multi-Forgery Detection Challenge，该挑战旨在检测包括但不限于图像编辑、图像合成等自动图像篡改行为，该挑战吸引了来自全球的 674 支队伍参与，其中 Top 10 队伍被邀请展示解决方案，最终 3 支队伍获得了奖励，本文旨在介绍这 3 支队伍的解决方案，以推进图像伪造检测领域的研究。

2022 年多类伪造检测竞赛：推进无约束和多元化伪造检测的前沿

Multi-Forgery Detection Challenge 2022: Push the Frontier of  Unconstrained and Diverse Forgery Detection

While great progress has been made recently in automatic image manipulation,
it has been limited to object centric images like faces or structured scene
datasets. In this work, we take a step towards general scene-level image
editing by developing an automatic interaction-free object removal model. Our
model learns to find and remove objects from general scene images using
image-level labels and unpaired data in a generative adversarial network (GAN)
framework. We achieve this with two key contributions: a two-stage editor
architecture consisting of a mask generator and image in-painter that
co-operate to remove objects, and a novel GAN based prior for the mask
generator that allows us to flexibly incorporate knowledge about object shapes.
We experimentally show on two datasets that our method effectively removes a
wide variety of objects using weak supervision only

本文旨在研发一种自动化、不需要交互即可实现目标物体移除的模型，其基于生成对抗网络框架，利用图像级别标签和非配对数据进行训练。通过两个关键贡献：一个由掩膜生成器和图像修复器组成的两阶段编辑器体系结构，以及一种基于 GAN 的先验模型，该模型能灵活地整合物体形状知识。我们在两个数据集上进行实验表明，该方法能够有效地去除各种不同的物体，仅需要较弱的监督学习。