融合多特征的轻量级深度伪造检测方法，适配低算力设备。
Lightweight Deepfake Detection Based on Multi-Feature Fusion
- 结合关键帧与纹理分析，降低模型计算负担。
- 融合HOG/LBP/KAZE特征，准确率达92%（FaceForensics++）和96%（Celeb-DFv2）。
- 适用于移动端等资源受限场景，兼顾效率与精度。
深度伪造技术利用基于深度学习的面部操控手段，无缝替换视频中的人脸，生成高度逼真的虚假内容。尽管该技术在媒体娱乐领域有积极应用，但其滥用可能引发身份盗用、网络欺凌及虚假信息传播等严重风险。深度学习与视觉认知的融合推动了数字媒体平台隐私风险防护的技术进步。本文提出一种高效轻量的深度伪造图像与视频检测方法，适用于计算资源有限的设备。为减轻深度学习模型的计算压力，本方法结合机器学习分类器、关键帧提取与纹理分析策略，并融合直方图方向梯度（HOG）、局部二值模式（LBP）及KAZE特征，通过随机森林、极端梯度提升、额外树与支持向量机算法进行评估。实验结果表明，特征级融合在FaceForensics++数据集上达到92%准确率，在Celeb-DFv2数据集上达96%。
原文摘要 · Abstract (English)
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.
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