Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videos
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.
Code (3)
Tasks
Image and Video Forgery DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Use of a Capsule Network to Detect Fake Images and Videos
The revolution in computer hardware, especially in graphics processing units and tensor processing units, has enabled significant advances in computer graphics and artificial intelligence algorithms. In addition to their…
Image and Video Forgery DetectionDARCCC: Detecting Adversaries by Reconstruction from Class Conditional Capsules
We present a simple technique that allows capsule models to detect adversarial images. In addition to being trained to classify images, the capsule model is trained to reconstruct the images from the pose parameters and …
Capsule GAN Using Capsule Network for Generator Architecture
This paper presents Capsule GAN, a Generative adversarial network using Capsule Network not only in the discriminator but also in the generator. Recently, Generative adversarial networks (GANs) has been intensively studi…
Generative Adversarial NetworkKernelized Capsule Networks
Capsule Networks attempt to represent patterns in images in a way that preserves hierarchical spatial relationships. Additionally, research has demonstrated that these techniques may be robust against adversarial perturb…
Gaussian ProcessesNovel Deep Learning Model for Traffic Sign Detection Using Capsule Networks
Convolutional neural networks are the most widely used deep learning algorithms for traffic signal classification till date but they fail to capture pose, view, orientation of the images because of the intrinsic inabilit…
Deep LearningTraffic Sign DetectionTraffic Sign Recognition