Lightweight Deepfake Detection Based on Multi-Feature Fusion

TL;DR

This paper introduces a lightweight deepfake detection method that combines multiple visual features and machine learning classifiers, achieving high accuracy on benchmark datasets while being suitable for resource-constrained devices.

Contribution

It proposes a novel multi-feature fusion approach integrating HOG, LBP, and KAZE features with machine learning classifiers for efficient deepfake detection.

Findings

Achieved 92% accuracy on FaceForensics++

Achieved 96% accuracy on Celeb-DFv2

Method is suitable for devices with limited computational resources

Abstract

Deepfake technology utilizes deep learning based face manipulation techniques to seamlessly replace faces in videos creating highly realistic but artificially generated content. Although this technology has beneficial applications in media and entertainment misuse of its capabilities may lead to serious risks including identity theft cyberbullying and false information. The integration of DL with visual cognition has resulted in important technological improvements particularly in addressing privacy risks caused by artificially generated deepfake images on digital media platforms. In this study we propose an efficient and lightweight method for detecting deepfake images and videos making it suitable for devices with limited computational resources. In order to reduce the computational burden usually associated with DL models our method integrates machine learning classifiers in combination with keyframing approaches and texture analysis. Moreover the features extracted with a histogram of oriented gradients (HOG) local binary pattern (LBP) and KAZE bands were integrated to evaluate using random forest extreme gradient boosting extra trees and support vector classifier algorithms. Our findings show a feature-level fusion of HOG LBP and KAZE features improves accuracy to 92% and 96% on FaceForensics++ and Celeb-DFv2 respectively.