A Graph Neural Network Model for Live Face Anti-Spoofing Detection Camera Systems | IEEE Journals & Magazine | IEEE Xplore

A Graph Neural Network Model for Live Face Anti-Spoofing Detection Camera Systems


Abstract:

As the demand for the Internet of Things (IoT) grows, it becomes crucial to possess systems capable of detecting any data leakage used for authentication. Within IoT came...Show More

Abstract:

As the demand for the Internet of Things (IoT) grows, it becomes crucial to possess systems capable of detecting any data leakage used for authentication. Within IoT camera systems based on facial bio-metric recognition, there is a risk of deepfake bypassed facial feature authentication due to the widespread use of deepfake video technologies, such as DeepFaceLive and expression manipulation. Traditional face anti-spoofing (FAS) detection techniques may struggle to detect real-time deepfake videos within IoT contexts. Moreover, constrained by the scale of FAS detection data sets, current detection models primarily focus on recognizing the entire face in videos, neglecting the intercomponent correlations of facial features. However, our investigation indicates that different parts of the face have varying impacts on deepfake detection. To address this issue, we segment the face into several regions within video frames and explore the relationships between these regions. Our approach involves constructing feature graphs that represent such correlations, aiming to leverage the relationships between facial regions and the temporal characteristics of real-time facial manipulation videos for use in live facial detection cameras. Initially, features for each facial region are extracted via convolutional neural networks (CNNs). Subsequently, with these features as vertices and their correlations as edges, a feature graph of the entire video is constructed. Ultimately, a graph neural network (GNN) is employed to determine whether the video has been tampered with. Experiments conducted on several publicly accessible data sets demonstrate that our proposed method outperforms other state-of-the-art FAS detection techniques in most scenarios. Thus, the aforementioned advanced GNN model exhibits exceptional performance in real-time deepfake detection tailored for live facial detection cameras.
Published in: IEEE Internet of Things Journal ( Volume: 11, Issue: 15, 01 August 2024)
Page(s): 25720 - 25730
Date of Publication: 11 April 2024

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