This paper proposes Characteristic Examples for effectively fingerprinting
deep neural networks, featuring high-robustness to the base model against model
pruning as well as low-transferability to unassociated models. This is the
first work taking both robustness and transferability into consideration for
generating realistic fingerprints, whereas current methods lack practical
assumptions and may incur large false positive rates. To achieve better
trade-off between robustness and transferability, we propose three kinds of
characteristic examples: vanilla C-examples, RC-examples, and LTRC-example, to
derive fingerprints from the original base model. To fairly characterize the
trade-off between robustness and transferability, we propose Uniqueness Score,
a comprehensive metric that measures the difference between robustness and
transferability, which also serves as an indicator to the false alarm problem.

本文提出了特征样例，有效地给深度神经网络生成指纹。为了更好地平衡鲁棒性和可传递性，我们提出了三类特征样例：普通 C 样例，RC 样例和 LTRC 样例，并且提出了独特性评分，这是测量鲁棒性和可传递性之间差异的综合指标，也是假警报问题的一个指示器。