Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videos
This addresses the challenge of media forgery detection for security applications, but it appears incremental as it adapts an existing method to a new domain.
The paper tackles the problem of detecting forged images and videos by using a capsule network to identify various spoofs, such as replay attacks and computer-generated videos, achieving results that extend capsule networks to inverse graphics problems.
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.