Generate, Segment and Refine: Towards Generic Manipulation Segmentation
This addresses the spread of fake news and misinformation by improving detection of manipulated images, though it is incremental as it builds on existing datasets and methods.
The paper tackles the problem of detecting manipulated images by addressing the lack of training data through a novel generation process that creates true positives and forces focus on boundary artifacts, achieving strong experimental results.
Detecting manipulated images has become a significant emerging challenge. The advent of image sharing platforms and the easy availability of advanced photo editing software have resulted in a large quantities of manipulated images being shared on the internet. While the intent behind such manipulations varies widely, concerns on the spread of fake news and misinformation is growing. Current state of the art methods for detecting these manipulated images suffers from the lack of training data due to the laborious labeling process. We address this problem in this paper, for which we introduce a manipulated image generation process that creates true positives using currently available datasets. Drawing from traditional work on image blending, we propose a novel generator for creating such examples. In addition, we also propose to further create examples that force the algorithm to focus on boundary artifacts during training. Strong experimental results validate our proposal.