Deep neural network (DNN) models have become a critical asset of the model
owner as training them requires a large amount of resource (i.e. labeled data).
Therefore, many fingerprinting schemes have been proposed to safeguard the
intellectual property (IP) of the model owner against mo
本研究提出了一种基于 GAN 的机器学习框架,能够通过先前训练的先验分布来生成指纹图像。在 loss 函数中添加合适的正则化项以实现指纹图像的连通性,在两个流行的指纹数据库中进行实验,结果表明模型能够生成非常逼真且类似于样本的指纹图像,并且具有良好的分布不同性。该模型的 FID 分数也能够取得良好的量化表现。