论文标题
关于发现甘斯产生的变形攻击
On the detection of morphing attacks generated by GANs
论文作者
论文摘要
最近的工作表明，基于GAN的变形攻击的可行性与基于具有里程碑意义的方法的成功率相似。这种新型的“深”形态可能需要开发新的足够检测器来保护面部识别系统。我们根据光谱特征和LBP直方图特征以及CNN模型探索简单的深度检测基线，包括在dataset和跨数据库中。我们观察到，简单的基于LBP的系统已经在数据内设置中非常准确，但是与概括斗争，这种现象通过将其中的几个系统融合在一起而部分缓解了这种现象。我们得出的结论是，对GAN图像检测有效的有效的重新连接是最有效的总体，达到了几乎完美的精度。但是，我们注意到，基于LBP的系统保持了一定的兴趣：除了其较低的计算需求以及相对于CNN而言，LBP+Resnet融合的可解释性提高，有时还会显示出仅相对于RESNET的性能提高的性能，这暗示基于LBP的系统还可以专注于有意义的信号，而CNN探测器不一定会选择有意义的信号。
Recent works have demonstrated the feasibility of GAN-based morphing attacks that reach similar success rates as more traditional landmark-based methods. This new type of "deep" morphs might require the development of new adequate detectors to protect face recognition systems. We explore simple deep morph detection baselines based on spectral features and LBP histograms features, as well as on CNN models, both in the intra-dataset and cross-dataset case. We observe that simple LBP-based systems are already quite accurate in the intra-dataset setting, but struggle with generalization, a phenomenon that is partially mitigated by fusing together several of those systems at score-level. We conclude that a pretrained ResNet effective for GAN image detection is the most effective overall, reaching close to perfect accuracy. We note however that LBP-based systems maintain a level of interest : additionally to their lower computational requirements and increased interpretability with respect to CNNs, LBP+ResNet fusions sometimes also showcase increased performance versus ResNet-only, hinting that LBP-based systems can focus on meaningful signal that is not necessarily picked up by the CNN detector.
