论文标题
面对粘贴攻击
Face Pasting Attack
论文作者
论文摘要
Cujo AI和Adversa AI主持了MLSEC面部识别挑战。目标是攻击具有针对性攻击的黑匣子面部识别模型。该模型恢复了目标级别的信心和隐形评分。为了使攻击被认为是成功的，目标阶级必须在所有阶层中具有最高的信心，并且隐身性必须至少为0.5。在我们的方法中，我们将目标的面粘贴到源图像中。通过利用位置，缩放，旋转和透明属性，我们到达了第三名。我们的方法每次攻击的最终得分大约有200个查询，成功攻击的最低率最低约7.7个查询。该代码可在https://github.com/bunni90/facepastingattack上找到。
Cujo AI and Adversa AI hosted the MLSec face recognition challenge. The goal was to attack a black box face recognition model with targeted attacks. The model returned the confidence of the target class and a stealthiness score. For an attack to be considered successful the target class has to have the highest confidence among all classes and the stealthiness has to be at least 0.5. In our approach we paste the face of a target into a source image. By utilizing position, scaling, rotation and transparency attributes we reached 3rd place. Our approach took approximately 200 queries per attack for the final highest score and about ~7.7 queries minimum for a successful attack. The code is available at https://github.com/bunni90/FacePastingAttack .
