141 citations · 766 across the 81 of their papers we have counts for
13 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 $\…
Identity-Aware Deep Face Hallucination via Adversarial Face Verification
Hadi Kazemi, Fariborz Taherkhani, Nasser M. Nasrabadi
In this paper, we address the problem of face hallucination by proposing a novel multi-scale generative adversarial network (GAN) architecture optimized for face verification. Firs…
Deep Sparse Band Selection for Hyperspectral Face Recognition
Fariborz Taherkhani, Jeremy Dawson, Nasser M. Nasrabadi
Hyperspectral imaging systems collect and process information from specific wavelengths across the electromagnetic spectrum. The fusion of multi-spectral bands in the visible spect…
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…
Zero-Shot Deep Hashing and Neural Network Based Error Correction for Face Template Protection
Veeru Talreja, Matthew C. Valenti, Nasser M. Nasrabadi
In this paper, we present a novel architecture that integrates a deep hashing framework with a neural network decoder (NND) for application to face template protection. It improves…
Attribute-Guided Coupled GAN for Cross-Resolution Face Recognition
Veeru Talreja, Fariborz Taherkhani, Matthew C Valenti +1
In this paper, we propose a novel attribute-guided cross-resolution (low-resolution to high-resolution) face recognition framework that leverages a coupled generative adversarial n…
Attribute-Guided Deep Polarimetric Thermal-to-visible Face Recognition
Seyed Mehdi Iranmanesh, Nasser M. Nasrabadi
In this paper, we present an attribute-guided deep coupled learning framework to address the problem of matching polarimetric thermal face photos against a gallery of visible faces…
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…
Detection and Statistical Modeling of Birth-Death Anomaly
Salman Mohamadi, Farhang Yeganegi, Nasser M Nasrabadi
Generally, anomaly detection has a great importance particularly in applied statistical signal processing. Here we provide a general framework in order to detect anomaly through th…
A data-driven proxy to Stoke's flow in porous media
Ali Takbiri-Borujeni, Hadi Kazemi, Nasser Nasrabadi
The objective for this work is to develop a data-driven proxy to high-fidelity numerical flow simulations using digital images. The proposed model can capture the flow field and pe…
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…
Using Deep Cross Modal Hashing and Error Correcting Codes for Improving the Efficiency of Attribute Guided Facial Image Retrieval
Veeru Talreja, Fariborz Taherkhani, Matthew C. Valenti +1
With benefits of fast query speed and low storage cost, hashing-based image retrieval approaches have garnered considerable attention from the research community. In this paper, we…
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…