25 research outputs found
On the Construction of Polar Codes for Achieving the Capacity of Marginal Channels
Achieving security against adversaries with unlimited computational power is
of great interest in a communication scenario. Since polar codes are capacity
achieving codes with low encoding-decoding complexity and they can approach
perfect secrecy rates for binary-input degraded wiretap channels in symmetric
settings, they are investigated extensively in the literature recently. In this
paper, a polar coding scheme to achieve secrecy capacity in non-symmetric
binary input channels is proposed. The proposed scheme satisfies security and
reliability conditions. The wiretap channel is assumed to be stochastically
degraded with respect to the legitimate channel and message distribution is
uniform. The information set is sent over channels that are good for Bob and
bad for Eve. Random bits are sent over channels that are good for both Bob and
Eve. A frozen vector is chosen randomly and is sent over channels bad for both.
We prove that there exists a frozen vector for which the coding scheme
satisfies reliability and security conditions and approaches the secrecy
capacity. We further empirically show that in the proposed scheme for
non-symmetric binary-input discrete memoryless channels, the equivocation rate
achieves its upper bound in the whole capacity-equivocation region
Deep Sketch-Photo Face Recognition Assisted by Facial Attributes
In this paper, we present a deep coupled framework to address the problem of
matching sketch image against a gallery of mugshots. Face sketches have the
essential in- formation about the spatial topology and geometric details of
faces while missing some important facial attributes such as ethnicity, hair,
eye, and skin color. We propose a cou- pled deep neural network architecture
which utilizes facial attributes in order to improve the sketch-photo
recognition performance. The proposed Attribute-Assisted Deep Con- volutional
Neural Network (AADCNN) method exploits the facial attributes and leverages the
loss functions from the facial attributes identification and face verification
tasks in order to learn rich discriminative features in a common em- bedding
subspace. The facial attribute identification task increases the inter-personal
variations by pushing apart the embedded features extracted from individuals
with differ- ent facial attributes, while the verification task reduces the
intra-personal variations by pulling together all the fea- tures that are
related to one person. The learned discrim- inative features can be well
generalized to new identities not seen in the training data. The proposed
architecture is able to make full use of the sketch and complementary fa- cial
attribute information to train a deep model compared to the conventional
sketch-photo recognition methods. Exten- sive experiments are performed on
composite (E-PRIP) and semi-forensic (IIIT-D semi-forensic) datasets. The
results show the superiority of our method compared to the state- of-the-art
models in sketch-photo recognition algorithm
Prosodic-Enhanced Siamese Convolutional Neural Networks for Cross-Device Text-Independent Speaker Verification
In this paper a novel cross-device text-independent speaker verification
architecture is proposed. Majority of the state-of-the-art deep architectures
that are used for speaker verification tasks consider Mel-frequency cepstral
coefficients. In contrast, our proposed Siamese convolutional neural network
architecture uses Mel-frequency spectrogram coefficients to benefit from the
dependency of the adjacent spectro-temporal features. Moreover, although
spectro-temporal features have proved to be highly reliable in speaker
verification models, they only represent some aspects of short-term acoustic
level traits of the speaker's voice. However, the human voice consists of
several linguistic levels such as acoustic, lexicon, prosody, and phonetics,
that can be utilized in speaker verification models. To compensate for these
inherited shortcomings in spectro-temporal features, we propose to enhance the
proposed Siamese convolutional neural network architecture by deploying a
multilayer perceptron network to incorporate the prosodic, jitter, and shimmer
features. The proposed end-to-end verification architecture performs feature
extraction and verification simultaneously. This proposed architecture displays
significant improvement over classical signal processing approaches and deep
algorithms for forensic cross-device speaker verification.Comment: Accepted in 9th IEEE International Conference on Biometrics: Theory,
Applications, and Systems (BTAS 2018
Polar Coding for Achieving the Capacity of Marginal Channels in Nonbinary-Input Setting
Achieving information-theoretic security using explicit coding scheme in
which unlimited computational power for eavesdropper is assumed, is one of the
main topics is security consideration. It is shown that polar codes are
capacity achieving codes and have a low complexity in encoding and decoding. It
has been proven that polar codes reach to secrecy capacity in the binary-input
wiretap channels in symmetric settings for which the wiretapper's channel is
degraded with respect to the main channel. The first task of this paper is to
propose a coding scheme to achieve secrecy capacity in asymmetric
nonbinary-input channels while keeping reliability and security conditions
satisfied. Our assumption is that the wiretap channel is stochastically
degraded with respect to the main channel and message distribution is
unspecified. The main idea is to send information set over good channels for
Bob and bad channels for Eve and send random symbols for channels that are good
for both. In this scheme the frozen vector is defined over all possible choices
using polar codes ensemble concept. We proved that there exists a frozen vector
for which the coding scheme satisfies reliability and security conditions. It
is further shown that uniform distribution of the message is the necessary
condition for achieving secrecy capacity.Comment: Accepted to be published in "51th Conference on Information Sciences
and Systems", Baltimore, Marylan
Robust Ensemble Morph Detection with Domain Generalization
Although a substantial amount of studies is dedicated to morph detection,
most of them fail to generalize for morph faces outside of their training
paradigm. Moreover, recent morph detection methods are highly vulnerable to
adversarial attacks. In this paper, we intend to learn a morph detection model
with high generalization to a wide range of morphing attacks and high
robustness against different adversarial attacks. To this aim, we develop an
ensemble of convolutional neural networks (CNNs) and Transformer models to
benefit from their capabilities simultaneously. To improve the robust accuracy
of the ensemble model, we employ multi-perturbation adversarial training and
generate adversarial examples with high transferability for several single
models. Our exhaustive evaluations demonstrate that the proposed robust
ensemble model generalizes to several morphing attacks and face datasets. In
addition, we validate that our robust ensemble model gain better robustness
against several adversarial attacks while outperforming the state-of-the-art
studies.Comment: Accepted in IJCB 202