# Use of a Capsule Network to Detect Fake Images and Videos

arXiv: https://arxiv.org/abs/1910.12467 · HTML: https://mlsanity.com/paper/1910.12467/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2019.

## Built on by (most cited first, 30 of 63)

- [Detecting Deepfakes with Self-Blended Images](https://mlsanity.com/paper/2204.08376/) (2022)
- [FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset](https://mlsanity.com/paper/2108.05080/) (2021)
- [Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization](https://mlsanity.com/paper/2210.14457/) (2022)
- [M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection](https://mlsanity.com/paper/2104.09770/) (2021)
- [Protecting Celebrities from DeepFake with Identity Consistency Transformer](https://mlsanity.com/paper/2203.01318/) (2022)
- [Face Forensics in the Wild](https://mlsanity.com/paper/2103.16076/) (2021)
- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [Deepfake Detection: A Comprehensive Survey from the Reliability Perspective](https://mlsanity.com/paper/2211.10881/) (2022)
- [A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials](https://mlsanity.com/paper/2205.05467/) (2022)
- [Improving Fairness in Deepfake Detection](https://mlsanity.com/paper/2306.16635/) (2023)
- [Initiative Defense against Facial Manipulation](https://mlsanity.com/paper/2112.10098/) (2021)
- [Explaining Deepfake Detection by Analysing Image Matching](https://mlsanity.com/paper/2207.09679/) (2022)
- [Evaluation of an Audio-Video Multimodal Deepfake Dataset using Unimodal and Multimodal Detectors](https://mlsanity.com/paper/2109.02993/) (2021)
- [DefakeHop: A Light-Weight High-Performance Deepfake Detector](https://mlsanity.com/paper/2103.06929/) (2021)
- [Green Learning: Introduction, Examples and Outlook](https://mlsanity.com/paper/2210.00965/) (2022)
- [Towards Solving the DeepFake Problem : An Analysis on Improving DeepFake Detection using Dynamic Face Augmentation](https://mlsanity.com/paper/2102.09603/) (2021)
- [Deepfake Videos in the Wild: Analysis and Detection](https://mlsanity.com/paper/2103.04263/) (2021)
- [Deepfake Detection Scheme Based on Vision Transformer and Distillation](https://mlsanity.com/paper/2104.01353/) (2021)
- [Deepfake Video Detection with Spatiotemporal Dropout Transformer](https://mlsanity.com/paper/2207.06612/) (2022)
- [Deep Convolutional Pooling Transformer for Deepfake Detection](https://mlsanity.com/paper/2209.05299/) (2022)
- [Exposing Deepfake Face Forgeries with Guided Residuals](https://mlsanity.com/paper/2205.00753/) (2022)
- [FaceGuard: Proactive Deepfake Detection](https://mlsanity.com/paper/2109.05673/) (2021)
- [PolyGlotFake: A Novel Multilingual and Multimodal DeepFake Dataset](https://mlsanity.com/paper/2405.08838/) (2024)
- [UnGANable: Defending Against GAN-based Face Manipulation](https://mlsanity.com/paper/2210.00957/) (2022)
- [VidGuard-R1: AI-Generated Video Detection and Explanation via Reasoning MLLMs and RL](https://mlsanity.com/paper/2510.02282/) (2025)
- [Analyzing Fairness in Deepfake Detection With Massively Annotated Databases](https://mlsanity.com/paper/2208.05845/) (2022)
- [AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake Detection](https://mlsanity.com/paper/2312.08675/) (2023)
- [Learning Pairwise Interaction for Generalizable DeepFake Detection](https://mlsanity.com/paper/2302.13288/) (2023)
- [Metric Learning for Anti-Compression Facial Forgery Detection](https://mlsanity.com/paper/2103.08397/) (2021)
- [Integrating Audio-Visual Features for Multimodal Deepfake Detection](https://mlsanity.com/paper/2310.03827/) (2023)

## Abstract (arXiv)

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 many beneficial applications in daily life and business, computer-generated/manipulated images and videos can be used for malicious purposes that violate security systems, privacy, and social trust. The deepfake phenomenon and its variations enable a normal user to use his or her personal computer to easily create fake videos of anybody from a short real online video. Several countermeasures have been introduced to deal with attacks using such videos. However, most of them are targeted at certain domains and are ineffective when applied to other domains or new attacks. In this paper, we introduce a capsule network that can detect various kinds of attacks, from presentation attacks using printed images and replayed videos to attacks using fake videos created using deep learning. It uses many fewer parameters than traditional convolutional neural networks with similar performance. Moreover, we explain, for the first time ever in the literature, the theory behind the application of capsule networks to the forensics problem through detailed analysis and visualization.