利用模板反演逼近最优变形攻击
Approximating Optimal Morphing Attacks using Template Inversion
AI总结：
本文提出基于模板反演的新型深度变形攻击方法，通过反演源图像嵌入均值获得理论最优变形嵌入，实验证明其在白盒和黑盒场景下可超越现有方法且速度更快。
摘要：
近期研究已证明人脸识别系统反演的可行性，即仅利用嵌入向量即可恢复出令人信服的人脸图像。我们利用此类模板反演模型，开发了一种新型深度变形攻击方法，该方法基于对理论最优变形嵌入的反演，而该嵌入由源图像人脸嵌入的平均值获得。我们对该方法的两种变体进行了实验：第一种利用完全自包含的嵌入到图像反演模型，第二种则利用预训练 StyleGAN 网络的合成网络以提高变形真实感。我们从多个源数据集生成变形攻击，并研究这些攻击对若干人脸识别网络的有效性。我们展示了我们的方法在有效性方面能够与先前基于深度学习变形生成的最先进方法相抗衡并经常超越之，在白盒和黑盒攻击场景中均是如此，并且运行速度更快。我们希望这能促进大规模深度变形数据集的构建，以用于训练检测模型。
英文摘要：
Recent works have demonstrated the feasibility of inverting face recognition systems, enabling to recover convincing face images using only their embeddings. We leverage such template inversion models to develop a novel type ofdeep morphing attack based on inverting a theoretical optimal morph embedding, which is obtained as an average of the face embeddings of source images. We experiment with two variants of this approach: the first one exploits a fully self-contained embedding-to-image inversion model, while the second leverages the synthesis network of a pretrained StyleGAN network for increased morph realism. We generate morphing attacks from several source datasets and study the effectiveness of those attacks against several face recognition networks. We showcase that our method can compete with and regularly beat the previous state of the art for deep-learning based morph generation in terms of effectiveness, both in white-box and black-box attack scenarios, and is additionally much faster to run. We hope this might facilitate the development of large scale deep morph datasets for training detection models.
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