SMIFD: Novel Social Media Image Forgery Detection Database

Abstract

Image forgery or manipulation changes the contents of a set of original images to create a new image. Unfortunately, manipulated images become a growing concern with respect to spreading misinformation via image sharing in the social media. Despite the availability of a large number of automatic Image Forgery Detection (IFD) methods, their evaluation in real-world benchmarks seems to be limited due to the lack of diverse datasets. Moreover, the motifs behind the manipulation remains unclear. This research aims to address these issues by proposing a novel social media IFD database, called SMIFD-500, to evaluate the efficiency and generalizability of the IFD methods. The unique property of this dataset is the availability of the technical and social attributes in its ground truth annotations. These will benefit the scientific community to develop efficient methods by exploiting such annotations. Moreover, it provides interesting statistics, which highlights the motifs of image manipulation from social science perspective.