Identifying Human Edited Images using a CNN

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 hu...

Full description

Saved in:

Bibliographic Details
Main Authors:	Lee, Jordan, Lin, Willy, Ntalis, Konstantinos, Shah, Anirudh, Tung, William, Wulff, Maxwell
Format:	Journal Article
Language:	English
Published:	08-01-2021
Subjects:	Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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.
DOI:	10.48550/arxiv.2101.03275