On the Detection of Digital Face Manipulation

TL;DR

This paper introduces an attention-based method for detecting and localizing manipulated facial images, demonstrating improved accuracy and interpretability using a new large-scale dataset of facial forgeries.

Contribution

It proposes an attention mechanism to enhance fake face detection and localization, along with creating a large-scale dataset for comprehensive analysis.

Findings

Attention mechanism improves detection accuracy

Enhanced localization of manipulated regions

Large-scale dataset enables thorough analysis

Abstract

Detecting manipulated facial images and videos is an increasingly important topic in digital media forensics. As advanced face synthesis and manipulation methods are made available, new types of fake face representations are being created which have raised significant concerns for their use in social media. Hence, it is crucial to detect manipulated face images and localize manipulated regions. Instead of simply using multi-task learning to simultaneously detect manipulated images and predict the manipulated mask (regions), we propose to utilize an attention mechanism to process and improve the feature maps for the classification task. The learned attention maps highlight the informative regions to further improve the binary classification (genuine face v. fake face), and also visualize the manipulated regions. To enable our study of manipulated face detection and localization, we collect a large-scale database that contains numerous types of facial forgeries. With this dataset, we perform a thorough analysis of data-driven fake face detection. We show that the use of an attention mechanism improves facial forgery detection and manipulated region localization.