# Lightweight Deepfake Detection Based on Multi-Feature Fusion

arXiv: https://arxiv.org/abs/2502.11763 · HTML: https://mlsanity.com/paper/2502.11763/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 62 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics](https://mlsanity.com/paper/1909.12962/) (2019)
- [Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks](https://mlsanity.com/paper/2310.00076/) (2023)
- [Video Face Manipulation Detection Through Ensemble of CNNs](https://mlsanity.com/paper/2004.07676/) (2020)
- [Inserting Anybody in Diffusion Models via Celeb Basis](https://mlsanity.com/paper/2306.00926/) (2023)
- [Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networks](https://mlsanity.com/paper/2406.04932/) (2024)
- [Text-image guided Diffusion Model for generating Deepfake celebrity interactions](https://mlsanity.com/paper/2309.14751/) (2023)
- [Efficient Learning of Interpretable Classification Rules](https://mlsanity.com/paper/2205.06936/) (2022)
- [CN-LBP: Complex Networks-based Local Binary Patterns for Texture Classification](https://mlsanity.com/paper/2105.06652/) (2021)

## Built on by (most cited first, 2 of 2)

- [Cross-Branch Orthogonality for Improved Generalization in Face Deepfake Detection](https://mlsanity.com/paper/2505.04888/) (2025)
- [Adaptive Gated Deepfake Detection for Low-Resolution and Resource-Constrained Environments](https://mlsanity.com/paper/2609.05320/) (2026)

## Abstract (arXiv)

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.