核心概念
A hybrid deep learning model combining Convolutional Neural Network, CapsuleNet, and LSTM can effectively detect deepfake videos while providing explainable insights into the classification decisions.
要約
The paper presents a novel approach to detecting deepfake videos using a hybrid deep learning model. The key highlights are:
The model combines Convolutional Neural Network (CNN), CapsuleNet, and Long Short-Term Memory (LSTM) to leverage both spatial and temporal features for deepfake detection.
The CNN-CapsuleNet architecture is used to extract discriminative features from video frames, while the LSTM layer captures the temporal inconsistencies across frames that are characteristic of deepfake videos.
The model is trained on the large-scale DFDC dataset, which contains over 100,000 real and deepfake video clips.
Explainable AI (XAI) techniques, specifically Gradient-weighted Class Activation Mapping (Grad-CAM), are used to visualize the salient regions in the input frames that the model focuses on for its classification decisions.
The proposed hybrid model achieves an 88% validation accuracy, outperforming a combined model approach that uses separate detection models for different types of manipulations.
The XAI analysis reveals that the model focuses on facial regions when classifying real videos, while the activation regions are less prominent in fake videos, indicating the model's ability to detect facial inconsistencies introduced by deepfake algorithms.
Overall, the paper demonstrates a robust and explainable deepfake detection solution that can be valuable in maintaining the integrity of online media.
統計
The model was trained on the DFDC dataset, which contains over 100,000 real and deepfake video clips.
引用
"The ease of accessibility and the increase of availability of deepfake creations have raised the issue of security."
"Deepfakes are increasing the public discomfort and distrust in all spheres."