论文标题
部分可观测时空混沌系统的无模型预测
DICTION:DynamIC robusT whIte bOx watermarkiNg scheme for deep neural networks
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Deep neural network (DNN) watermarking is a suitable method for protecting the ownership of deep learning (DL) models. It secretly embeds an identifier (watermark) within the model, which can be retrieved by the owner to prove ownership. In this paper, we first provide a unified framework for white box DNN watermarking schemes. It includes current state-of-the-art methods outlining their theoretical inter-connections. Next, we introduce DICTION, a new white-box Dynamic Robust watermarking scheme, we derived from this framework. Its main originality stands on a generative adversarial network (GAN) strategy where the watermark extraction function is a DNN trained as a GAN discriminator taking the target model to watermark as a GAN generator with a latent space as the input of the GAN trigger set. DICTION can be seen as a generalization of DeepSigns which, to the best of our knowledge, is the only other Dynamic white-box watermarking scheme from the literature. Experiments conducted on the same model test set as Deepsigns demonstrate that our scheme achieves much better performance. Especially, with DICTION, one can increase the watermark capacity while preserving the target model accuracy at best and simultaneously ensuring strong watermark robustness against a wide range of watermark removal and detection attacks.
