用极简融合模块提升伪造人脸检测精度，模型更小效果更好。
Lightweight Complementary-Cue Fusion for Robust Video Face Forgery Detection
- 用轻量级模块融合两种人工设计特征，不依赖大模型。
- 在两个数据集上AUC分别提升至78.6%和74.9%，增益超4%。
- 适合追求高效部署的伪造检测应用，尤其资源受限场景。
当前人脸视频伪造检测器多采用宽网络或双流结构。本文表明，仅通过一个轻量级融合模块，将两种手工设计的特征组合，即可实现更高准确率且模型更小。基于Xception基线（2190万参数），构建了两个检测器：LFWS在1×1卷积中融合低频小波去噪特征（WDF）与空间相位浅层学习（SPSL）提取的相位谱通道；LFWL则融合WDF与局部二值模式（LBP）。该额外模块仅增加292个参数，总参数量仍为2190万，小于F3Net（2250万）且不足SRM（5530万）的一半。即便如此，融合模型在FaceForensics++上的平均AUC从74.8%提升至78.6%，在DFDC-Preview上从70.5%升至74.9%，分别提高3.8%和4.4%。在八个公开基准上持续优于F3Net、SRM和SPSL，无需额外数据或测试时增强。结果表明，精心配对的手工特征经轻量融合后，可提供媲美甚至超越频率类方法的鲁棒性，同时成本显著降低。研究提示应重新审视伪造检测中过度依赖模型规模的设计思路。
原文摘要 · Abstract (English)
Current face video forgery detectors use wide or dual-stream backbones. We show that a single, lightweight fusion of two handcrafted cues can achieve higher accuracy with a much smaller model. Based on the Xception baseline model (21.9 million parameters), we build two detectors: LFWS, which adds a 1x1 convolution to combine a low-frequency Wavelet-Denoised Feature (WDF) with a phase-spectrum channel derived from Spatial-Phase Shallow Learning (SPSL), and LFWL, which merges WDF with Local Binary Patterns (LBP) in the same way. This extra module adds only 292 parameters, keeping the total at 21.9 million, smaller than F3Net (22.5 million) and less than half the size of SRM (55.3 million). Even with this minimal overhead, the fused models increase the average area under the curve (AUC) from 74.8% to 78.6% on FaceForensics++ and from 70.5% to 74.9% on DFDC-Preview, gains of 3.8% and 4.4% over the Xception baseline. They also consistently outperform F3Net, SRM, and SPSL in eight public benchmarks, without extra data or test-time augmentation. These results show that carefully paired, handcrafted features, combined through the lightweight fusion block, can provide competitive robustness at a significantly lower cost than comparable frequency-based detectors. Our findings suggest a need to reevaluate scale-driven design choices in face video forgery detection.
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