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 ...

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 ...

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 ...

In autoencoders, the image must be unrolled into a single vector and the network must be built following the constraint on the number of inputs. The block diagram of a Convolutional Autoencoder is ...