The effect of recurrence on adversarial robustness

Traditionally, convolutional neural networks are feedforward networks with a deep and complex hierarchy. Conversely, the human brain has a relatively shallow hierarchy with recurrent connections. Replicating this recurrence may allow for shallower and easier to understand computer vision models that may possess characteristics usually attributed...

On the Sequential Data Models in Side-Channel Analysis: RNN, LSTM, GRU Hyperparameters, Autoencoder and Embedding Layer

A side-channel attack is performed by analyzing unwanted physical leakage to achieve a more effective attack on the cryptographic key. An attacker performs a profiled attack when he has a physical and identical copy of the target device, meaning the attacker is in full control of the target device. Therefore, these profiled attacks are known as...

Application of Deep Learning for Spacecraft Fault Detection and Isolation

Spacecraft require high availability, autonomous operation, and a high degree of mission success. Spacecraft use sensors, such as star trackers and GPS, and actuators, such as reaction wheels, to reach and maintain a correct attitude and position. Failures in these components will have a significant negative impact on the success of the mission,...

Efficient Recurrent Residual Networks Improved by Feature Transfer

Over the past several years, deep and wide neural networks have achieved great success in many tasks. However, in real life applications, because the gains usually come at a cost in terms of the system resources (e.g., memory, computation and power consumption), it is impractical to run top-performing but heavy networks such as VGGNet and...