Determining modified versions of social media images

Abstract Social media platforms usually contain several modified versions of an image. This proliferation of versions questions the trust of social media images. We propose a novel framework to find modified versions of social media images using only their metadata. We consider several aspects to determine if an image is a modified version of another image. These aspects include topic of an image, spatio-temporal information, and semantic similarity. We first do topic modeling to find images linked to the same context. Secondly, we perform spatio-temporal clustering to group spatio-temporally close images. Finally, we perform hierarchical clustering to form more precise clusters of versions. Notably, the proposed framework also considers modifications introduced in an image’s metadata while determining versions of the image. Modifications in social media images pose a significant challenge to correctly cluster versions together as a version may exhibit significant deviations from its original image. We address this issue by exploring inconsistencies in the image metadata. These inconsistencies are reflective of the changes in an image. We validate our model on a fact-checked image verification corpus and the Multimodal C4 dataset. We achieve around 95% accuracy, validating the effectiveness of the proposed approach.