To Örebro University

Image forgery/manipulation is one of the most alarming topics and becomes a major concern about different social media platforms regarding one’s privacy and safety. Therefore, the detection of the manipulated images is of immense interest to the researchers in the recent years. Despite the availability of numerous image forgery detection (IFD) datasets, very few particularly address the actual challenge by collecting the manipulated images from real-world scenario, e.g., collection of images from social media. Consequently, the contextual knowledge behind using the manipulated images remains unachieved. In order to address these issues, we propose an indigenous social media image forgery detection database, naming SMIFD-1000. This dataset provides rich annotations from several aspects: (a) image level: image regions that helps to classify pixel-level information; (b) forgery type: provide rich information about manipulation and (c) target and motif of manipulations: provide contextual rich knowledge about manipulation, which is significantly important from the perspective of social science. Finally, we would examine and benchmark the effectiveness of several publicly available algorithms on this dataset to demonstrate its usefulness. Results show that the dataset is highly challenging and will serve as an important benchmark for the existing and future IFD algorithms.