8 research outputs found
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
Towards Generalizable Morph Attack Detection with Consistency Regularization
Though recent studies have made significant progress in morph attack
detection by virtue of deep neural networks, they often fail to generalize well
to unseen morph attacks. With numerous morph attacks emerging frequently,
generalizable morph attack detection has gained significant attention. This
paper focuses on enhancing the generalization capability of morph attack
detection from the perspective of consistency regularization. Consistency
regularization operates under the premise that generalizable morph attack
detection should output consistent predictions irrespective of the possible
variations that may occur in the input space. In this work, to reach this
objective, two simple yet effective morph-wise augmentations are proposed to
explore a wide space of realistic morph transformations in our consistency
regularization. Then, the model is regularized to learn consistently at the
logit as well as embedding levels across a wide range of morph-wise augmented
images. The proposed consistency regularization aligns the abstraction in the
hidden layers of our model across the morph attack images which are generated
from diverse domains in the wild. Experimental results demonstrate the superior
generalization and robustness performance of our proposed method compared to
the state-of-the-art studies.Comment: Accepted to the IEEE International Joint Conference on Biometrics
(IJCB), 202
CCFace: Classification Consistency for Low-Resolution Face Recognition
In recent years, deep face recognition methods have demonstrated impressive
results on in-the-wild datasets. However, these methods have shown a
significant decline in performance when applied to real-world low-resolution
benchmarks like TinyFace or SCFace. To address this challenge, we propose a
novel classification consistency knowledge distillation approach that transfers
the learned classifier from a high-resolution model to a low-resolution
network. This approach helps in finding discriminative representations for
low-resolution instances. To further improve the performance, we designed a
knowledge distillation loss using the adaptive angular penalty inspired by the
success of the popular angular margin loss function. The adaptive penalty
reduces overfitting on low-resolution samples and alleviates the convergence
issue of the model integrated with data augmentation. Additionally, we utilize
an asymmetric cross-resolution learning approach based on the state-of-the-art
semi-supervised representation learning paradigm to improve discriminability on
low-resolution instances and prevent them from forming a cluster. Our proposed
method outperforms state-of-the-art approaches on low-resolution benchmarks,
with a three percent improvement on TinyFace while maintaining performance on
high-resolution benchmarks.Comment: 2023 IEEE International Joint Conference on Biometrics (IJCB
Multi-Context Dual Hyper-Prior Neural Image Compression
Transform and entropy models are the two core components in deep image
compression neural networks. Most existing learning-based image compression
methods utilize convolutional-based transform, which lacks the ability to model
long-range dependencies, primarily due to the limited receptive field of the
convolution operation. To address this limitation, we propose a
Transformer-based nonlinear transform. This transform has the remarkable
ability to efficiently capture both local and global information from the input
image, leading to a more decorrelated latent representation. In addition, we
introduce a novel entropy model that incorporates two different hyperpriors to
model cross-channel and spatial dependencies of the latent representation. To
further improve the entropy model, we add a global context that leverages
distant relationships to predict the current latent more accurately. This
global context employs a causal attention mechanism to extract long-range
information in a content-dependent manner. Our experiments show that our
proposed framework performs better than the state-of-the-art methods in terms
of rate-distortion performance.Comment: Accepted to IEEE 22 International Conference on Machine Learning
and Applications 2023 (ICMLA) - Selected for Oral Presentatio
AAFACE: Attribute-aware Attentional Network for Face Recognition
In this paper, we present a new multi-branch neural network that
simultaneously performs soft biometric (SB) prediction as an auxiliary modality
and face recognition (FR) as the main task. Our proposed network named AAFace
utilizes SB attributes to enhance the discriminative ability of FR
representation. To achieve this goal, we propose an attribute-aware attentional
integration (AAI) module to perform weighted integration of FR with SB feature
maps. Our proposed AAI module is not only fully context-aware but also capable
of learning complex relationships between input features by means of the
sequential multi-scale channel and spatial sub-modules. Experimental results
verify the superiority of our proposed network compared with the
state-of-the-art (SoTA) SB prediction and FR methods.Comment: Accepted to IEEE International Conference on Image
Processing (ICIP 2023) as an oral presentatio