用自适应空间算子提升人脸识别防伪能力，仅需单帧彩色图像。
Face Presentation Attack Detection via Content-Adaptive Spatial Operators
- 在MobileNetV3中引入内容自适应空间算子，增强局部伪造特征捕捉能力。
- 在多个数据集上实现超98%准确率，最高达100%，误报率低至0.00%。
- 模型轻量高效，适合移动端部署，无需额外传感器或时间序列信息。
人脸呈现攻击检测（FacePAD）对防范打印、重放和面具欺骗至关重要。本文提出CASO-PAD，一种仅使用RGB单帧图像的轻量级模型，通过在MobileNetV3中引入内容自适应空间算子（involution），更有效地捕捉局部伪造线索。与共享空间卷积核不同，该算子根据输入生成位置特异、通道共享的核，以极小开销提升空间选择性。CASO-PAD参数量仅3.6M，计算量0.64 GFLOPs（256×256输入），采用标准二元交叉熵端到端训练。在Replay-Attack、Replay-Mobile、ROSE-Youtu和OULU-NPU上的实验表明，测试准确率分别为100%/100%/98.9%/99.7%，AUC为1.00/1.00/0.9995/0.9999，HTER为0.00%/0.00%/0.82%/0.44%。在大规模SiW-Mv2 Protocol-1基准上，准确率达95.45%，HTER为3.11%，EER为3.13%，表明其在真实复杂攻击下的鲁棒性。消融实验显示，将自适应算子置于网络尾部并适度分组共享可获得最佳精度-效率平衡。整体而言，CASO-PAD为移动设备提供了无需辅助传感器或时序堆叠的实用化高鲁棒性人脸防伪方案。
原文摘要 · Abstract (English)
Face presentation attack detection (FacePAD) is critical for securing facial authentication against print, replay, and mask-based spoofing. This paper proposes CASO-PAD, an RGB-only, single-frame model that enhances MobileNetV3 with content-adaptive spatial operators (involution) to better capture localized spoof cues. Unlike spatially shared convolution kernels, the proposed operator generates location-specific, channel-shared kernels conditioned on the input, improving spatial selectivity with minimal overhead. CASO-PAD remains lightweight (3.6M parameters; 0.64 GFLOPs at $256\times256$) and is trained end-to-end using a standard binary cross-entropy objective. Extensive experiments on Replay-Attack, Replay-Mobile, ROSE-Youtu, and OULU-NPU demonstrate strong performance, achieving 100/100/98.9/99.7\% test accuracy, AUC of 1.00/1.00/0.9995/0.9999, and HTER of 0.00/0.00/0.82/0.44\%, respectively. On the large-scale SiW-Mv2 Protocol-1 benchmark, CASO-PAD further attains 95.45\% accuracy with 3.11\% HTER and 3.13\% EER, indicating improved robustness under diverse real-world attacks. Ablation studies show that placing the adaptive operator near the network head and using moderate group sharing yields the best accuracy--efficiency balance. Overall, CASO-PAD provides a practical pathway for robust, on-device FacePAD with mobile-class compute and without auxiliary sensors or temporal stacks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。