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Improving Face Presentation Attack Detection Through Deformable Convolution and Transfer Learning
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AI
DOI：10.1109/ACCESS.2025.3541546.png)
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摘要
En 中文
Face presentation attack detection (PAD) is essential for ensuring the security and reliability of face recognition systems by preventing unauthorized access through spoofing attempts. Attackers can exploit various methods, such as printed photos, video replays, paper masks, 3D masks, or makeup, to imitate a legitimate user's biometric traits. In this paper, we propose an enhanced face PAD solution that leverages the deformable convolutional layer within the MobileNetV2 architecture to improve detection accuracy. By replacing the standard convolution layer with a Deformable ConvNets V2, the proposed model adapts dynamically to spatial distortions, capturing more detailed and robust features for effective face PAD. Extensive experiments on the Replay-Attack, Replay-Mobile, ROSE-Youtu, OULU-NPU, and SiW-Mv2 datasets validate the superiority of the proposed approach. The method achieves a half total error rate (HTER) of 0.0% on both the Replay-Attack and Replay-Mobile datasets, 1.26% on ROSE-Youtu, 4.88% on SiW-Mv2, and an ACER of 0.208% on OULU-NPU, outperforming several existing methods. These results highlight the robustness and effectiveness of our approach in safeguarding face recognition systems against presentation attacks.
Keyword:
Deep learning
anti-spoofing
face liveness detection
deformable convolution
presentation attack detection
Deep learning
anti-spoofing
face liveness detection
deformable convolution
presentation attack detection
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Displacement; steeper gradient of generalization of avoidance than of approach with age of habit controlled.位移；在控制习惯年龄的情况下，回避行为的泛化梯度比趋近行为的泛化梯度更陡峭。
PREAI
分享收藏Fully supervised contrastive learning in latent space for face presentation attack detection.png)
APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
分享收藏分享收藏Client-specific anomaly detection for face presentation attack detectionPATTERN RECOGNITION
OAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏
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