Predicting manipulated regions in deepfake videos using convolutional vision transformers

Mohan  Bhandari; Sushant Shrestha; Utsab   Karki; Santosh  Adhikari; Rajan   Gaihre

doi:10.59400/cai.v2i2.1409

Predicting manipulated regions in deepfake videos using convolutional vision transformers

Mohan Bhandari Department of Science and Technology, Samriddhi College, Lokanthali, Bhaktapur 44800, Nepal
Sushant Shrestha Department of Computer Engineering, Kantipur Engineering College, Dhapakhel, Lalitpur 44700, Nepal
Utsab Karki Department of Computer Engineering, Kantipur Engineering College, Dhapakhel, Lalitpur 44700, Nepal
Santosh Adhikari Department of Computer Engineering, Kantipur Engineering College, Dhapakhel, Lalitpur 44700, Nepal
Rajan Gaihre Department of Computer Engineering, Kantipur Engineering College, Dhapakhel, Lalitpur 44700, Nepal

Ariticle ID: 1409

426 Views, 173 PDF Downloads

DOI: https://doi.org/10.59400/cai.v2i2.1409

Keywords: face detection; machine learning; vision transformer; convolution neural networks; Grad-CAM

Abstract

Deepfake technology, which uses artificial intelligence to create and manipulate realistic synthetic media, poses a serious threat to the trustworthiness and integrity of digital content. Deepfakes can be used to generate, swap, or modify faces in videos, altering the appearance, identity, or expression of individuals. This study presents an approach for deepfake detection, based on a convolutional vision transformer (CViT), a hybrid model that combines convolutional neural networks (CNNs) and vision transformers (ViTs). The proposed study uses a 20-layer CNN to extract learnable features from face images, and a ViT to classify them into real or fake categories. The study also employs MTCNN, a multi-task cascaded network, to detect and align faces in videos, improving the accuracy and efficiency of the face extraction process. The method is assessed using the FaceForensics++ dataset, which comprises 15,800 images sourced from 1600 videos. With an 80:10:10 split ratio, the experimental results show that the proposed method achieves an accuracy of 92.5% and an AUC of 0.91. We use Gradient-Weighted Class Activation Mapping (Grad-CAM) visualization that highlights distinctive image regions used for making a decision. The proposed method demonstrates a high capability of detecting and distinguishing between genuine and manipulated videos, contributing to the enhancement of media authenticity and security.

References

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Published

2024-07-01

How to Cite

Bhandari, M., Shrestha, S., Karki, U., Adhikari, S., & Gaihre, R. (2024). Predicting manipulated regions in deepfake videos using convolutional vision transformers. Computing and Artificial Intelligence, 2(2), 1409. https://doi.org/10.59400/cai.v2i2.1409

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