Unmasking Deepfake Faces from Videos Using An Explainable Cost-Sensitive Deep Learning Approach

TL;DR

This paper presents a resource-efficient, explainable, cost-sensitive deep learning approach using pre-trained CNNs to accurately detect deepfake faces in videos, addressing dataset imbalance and demonstrating high accuracy on benchmark datasets.

Contribution

The study introduces a novel cost-sensitive deep learning method with pre-trained CNNs for deepfake detection, emphasizing model adaptability and effectiveness in video face recognition.

Findings

XceptionNet achieved 98% accuracy on CelebDf-V2.

InceptionResNetV2 achieved 94% accuracy on FaceForensics++.

Key frame extraction improved processing efficiency.

Abstract

Deepfake technology is widely used, which has led to serious worries about the authenticity of digital media, making the need for trustworthy deepfake face recognition techniques more urgent than ever. This study employs a resource-effective and transparent cost-sensitive deep learning method to effectively detect deepfake faces in videos. To create a reliable deepfake detection system, four pre-trained Convolutional Neural Network (CNN) models: XceptionNet, InceptionResNetV2, EfficientNetV2S, and EfficientNetV2M were used. FaceForensics++ and CelebDf-V2 as benchmark datasets were used to assess the performance of our method. To efficiently process video data, key frame extraction was used as a feature extraction technique. Our main contribution is to show the models adaptability and effectiveness in correctly identifying deepfake faces in videos. Furthermore, a cost-sensitive neural network method was applied to solve the dataset imbalance issue that arises frequently in deepfake detection. The XceptionNet model on the CelebDf-V2 dataset gave the proposed methodology a 98% accuracy, which was the highest possible whereas, the InceptionResNetV2 model, achieves an accuracy of 94% on the FaceForensics++ dataset. Source Code: https://github.com/Faysal-MD/Unmasking-Deepfake-Faces-from-Videos-An-Explainable-Cost-Sensitive-Deep-Learning-Approach-IEEE2023