20 citations · 60 across the 24 of their papers we have counts for
5 papers · 1 filter
SmoothFool: An Efficient Framework for Computing Smooth Adversarial Perturbations
Ali Dabouei, Sobhan Soleymani, Fariborz Taherkhani +2
Deep neural networks are susceptible to adversarial manipulations in the input domain. The extent of vulnerability has been explored intensively in cases of -bounded and $\…
Defending Against Adversarial Iris Examples Using Wavelet Decomposition
Sobhan Soleymani, Ali Dabouei, Jeremy Dawson +1
Deep neural networks have presented impressive performance in biometric applications. However, their performance is highly at risk when facing carefully crafted input samples known…
Adversarial Examples to Fool Iris Recognition Systems
Sobhan Soleymani, Ali Dabouei, Jeremy Dawson +1
Adversarial examples have recently proven to be able to fool deep learning methods by adding carefully crafted small perturbation to the input space image. In this paper, we study…
Learning to Authenticate with Deep Multibiometric Hashing and Neural Network Decoding
Veeru Talreja, Sobhan Soleymani, Matthew C. Valenti +1
In this paper, we propose a novel multimodal deep hashing neural decoder (MDHND) architecture, which integrates a deep hashing framework with a neural network decoder (NND) to crea…
GASL: Guided Attention for Sparsity Learning in Deep Neural Networks
Amirsina Torfi, Rouzbeh A. Shirvani, Sobhan Soleymani +1
The main goal of network pruning is imposing sparsity on the neural network by increasing the number of parameters with zero value in order to reduce the architecture size and the…