In this paper, our idea is to propose a general steganographic framework for neural network models, embedding secret data during the network training process to obtain a stego network for covert ...

Exploiting invariances in data is crucial for neural networks to learn efficient representations and to make accurate predictions. Translation invariance is a key symmetry in image processing and lies ...

Convolutional neural networks (CNNs) are a type of neural network that is designed to capture increasingly more complex features within its input data. To do this, CNNs are constructed from a ...

The design of metamaterials which support unique optical responses is the basis for most thin-film nanophotonic applications. In practice, this inverse design (ID) problem can be difficult to solve ...

They then used 90 percent of these images to train a convolutional neural network to recognize the difference and then tested the neural net on the remaining 10 percent of the images.

In this paper, our idea is to propose a general steganographic framework for neural network models, embedding secret data during the network training process to obtain a stego network for covert ...