Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videos

TL;DR

This paper introduces a capsule network-based method for detecting various types of forged images and videos, aiming to provide a robust solution against evolving media manipulation techniques.

Contribution

It applies capsule networks to media forensics, extending their use beyond inverse graphics to detect diverse spoofing attacks in images and videos.

Findings

Effective detection of replay and computer-generated forgeries

Robustness against various spoofing techniques

Extension of capsule networks to media forensics

Abstract

Recent advances in media generation techniques have made it easier for attackers to create forged images and videos. State-of-the-art methods enable the real-time creation of a forged version of a single video obtained from a social network. Although numerous methods have been developed for detecting forged images and videos, they are generally targeted at certain domains and quickly become obsolete as new kinds of attacks appear. The method introduced in this paper uses a capsule network to detect various kinds of spoofs, from replay attacks using printed images or recorded videos to computer-generated videos using deep convolutional neural networks. It extends the application of capsule networks beyond their original intention to the solving of inverse graphics problems.