Multimodal Forgery Detection Using Ensemble Learning

• Introduction
• Getting Started
• Citing
• Contact

Introduction

This repository contains the official implementation (PyTorch) of (Multimodal Forgery Detection Using Ensemble Learning) proposed in APSIPA Paper 2022 (Ammarah Hashmi, Sahibzada Adil Shahzad).

In this paper, we focus on the multimodal forgery detection task and propose a deep forgery detection method based on audiovisual ensemble learning. The proposed method consists of four parts, namely a Video Network, an Audio Network, an Audiovisual Network, and a Voting Module.

Given a video, the proposed multimodal and ensemble learning system can identify whether it is fake or real.

Experimental results on a recently released multimodal (FakeAVCeleb dataset) show that the proposed method achieves 89% accuracy, significantly outperforming existing models.

You can access the FakeAVCeleb Dataset through this dataset site.

Please click the link to access the poster of the paper.

Getting Started

Code is releasing Soon...!

Citing

If you find our repository useful, please consider giving a ⭐ and cite our paper.

@inproceedings{hashmi2022multimodal,
  title={Multimodal Forgery Detection Using Ensemble Learning},
  author={Hashmi, Ammarah and Shahzad, Sahibzada Adil and Ahmad, Wasim and Lin, Chia Wen and Tsao, Yu and Wang, Hsin-Min},
  booktitle={2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)},
  pages={1524--1532},
  year={2022},
  organization={IEEE}
}

Contact

If you have any question, feel free to send an email at hashmiammarah0@gmail.com .