We present evidence that many common convolutional neural networks (CNNs) trained for face verification learn functions that are nearly equivalent under rotation. More specifically, we demonstrate that one face verification model's embeddings (i.e. last--layer activations) can be compared directly to another model's embeddings after only a rotation or linear transformation, with little performance penalty. This finding is demonstrated using IJB-C 1:1 verification across the combinations of ten modern off-the-shelf CNN-based face verification models which vary in training dataset, CNN architecture, way of using angular loss, or some combination of the 3, and achieve a mean true accept rate of 0.96 at a false accept rate of 0.01. When instead evaluating embeddings generated from two CNNs, where one CNN's embeddings are mapped with a linear transformation, the mean true accept rate drops to 0.95 using the same verification paradigm. Restricting these linear maps to only perform rotation produces a mean true accept rate of 0.91. These mappings' existence suggests that a common representation is learned by models with variation in training or structure. A discovery such as this likely has broad implications, and we provide an application in which face embeddings can be de-anonymized using a limited number of samples.

翻译：我们提供了证据,证明许多接受过面对面核查培训的普通革命神经网络(CNNs)在轮换期间学习了几乎等效的功能。更具体地说,我们证明,可以将一个面对面的核查模型的嵌入率(即最后一层激活)直接比作另一个模型的嵌入率(仅经过旋转或线性变换后,几乎没有性能处罚。这个结果通过综合十种现代现成CNN现成的面部核查模型(10个现代现现现现现现现现的光线性网络)的核查模型(这10个模型在培训数据集、CNN架构、使用角损失的方式或3的某种组合方面各不相同),以及达到一个以0.01的虚假接受率0.96的平均真实接受率。在评价两个CNN的嵌入率时,一个CNN的嵌入率是线性变的,而一个CNN的嵌入率是用线性变的,使用相同的核查范式将平均接受率降至0.95。将这些线性地图限制为只进行轮换,结果为0.91的平均真实接受率。这些地图的存在表明,一个共同的表述方式是通过培训或结构变换式模型来学习的模型,而我们可以提供这种近似的样本。