Identifying Human Edited Images using a CNN
This work tackles the problem of identifying human-edited images, specifically those altered using popular mobile applications like FaceTune and Pixlr, which is important for digital forensics and content authentication.
This paper addresses the lack of a dataset for detecting mobile application-based photo manipulations by presenting a generative model that approximates the distribution of human face edits. The authors also propose a method for detecting manipulations made with FaceTune and Pixlr.
Most non-professional photo manipulations are not made using propriety software like Adobe Photoshop, which is expensive and complicated to use for the average consumer selfie-taker or meme-maker. Instead, these individuals opt for user friendly mobile applications like FaceTune and Pixlr to make human face edits and alterations. Unfortunately, there is no existing dataset to train a model to classify these type of manipulations. In this paper, we present a generative model that approximates the distribution of human face edits and a method for detecting Facetune and Pixlr manipulations to human faces.