12,862 research outputs found
Convolutional Neural Networks for Attribute-based Active Authentication on Mobile Devices
We present a Deep Convolutional Neural Network (DCNN) architecture for the
task of continuous authentication on mobile devices. To deal with the limited
resources of these devices, we reduce the complexity of the networks by
learning intermediate features such as gender and hair color instead of
identities. We present a multi-task, part-based DCNN architecture for attribute
detection that performs better than the state-of-the-art methods in terms of
accuracy. As a byproduct of the proposed architecture, we are able to explore
the embedding space of the attributes extracted from different facial parts,
such as mouth and eyes, to discover new attributes. Furthermore, through
extensive experimentation, we show that the attribute features extracted by our
method outperform the previously presented attribute-based method and a
baseline LBP method for the task of active authentication. Lastly, we
demonstrate the effectiveness of the proposed architecture in terms of speed
and power consumption by deploying it on an actual mobile device.Comment: Accepted in BTAS 201
Explainable Black-Box Attacks Against Model-based Authentication
Establishing unique identities for both humans and end systems has been an
active research problem in the security community, giving rise to innovative
machine learning-based authentication techniques. Although such techniques
offer an automated method to establish identity, they have not been vetted
against sophisticated attacks that target their core machine learning
technique. This paper demonstrates that mimicking the unique signatures
generated by host fingerprinting and biometric authentication systems is
possible. We expose the ineffectiveness of underlying machine learning
classification models by constructing a blind attack based around the query
synthesis framework and utilizing Explainable-AI (XAI) techniques. We launch an
attack in under 130 queries on a state-of-the-art face authentication system,
and under 100 queries on a host authentication system. We examine how these
attacks can be defended against and explore their limitations. XAI provides an
effective means for adversaries to infer decision boundaries and provides a new
way forward in constructing attacks against systems using machine learning
models for authentication
Cross-Domain Deep Face Matching for Real Banking Security Systems
Ensuring the security of transactions is currently one of the major
challenges that banking systems deal with. The usage of face for biometric
authentication of users is attracting large investments from banks worldwide
due to its convenience and acceptability by people, especially in cross-domain
scenarios, in which facial images from ID documents are compared with digital
self-portraits (selfies) for the automated opening of new checking accounts,
e.g, or financial transactions authorization. Actually, the comparison of
selfies and IDs has also been applied in another wide variety of tasks
nowadays, such as automated immigration control. The major difficulty in such
process consists in attenuating the differences between the facial images
compared given their different domains. In this work, in addition to collecting
a large cross-domain face dataset, with 27,002 real facial images of selfies
and ID documents (13,501 subjects) captured from the databases of the major
public Brazilian bank, we propose a novel architecture for such cross-domain
matching problem based on deep features extracted by two well-referenced
Convolutional Neural Networks (CNN). Results obtained on the dataset collected,
called FaceBank, with accuracy rates higher than 93%, demonstrate the
robustness of the proposed approach to the cross-domain face matching problem
and its feasible application in real banking security systems
Transparent Face Recognition in the Home Environment
The BASIS project is about the secure application of transparent biometrics in the home environment. Due to transparency and home-setting requirements there is variance in appearance of the subject. An other problem which needs attention is the extraction of features. The quality of the extracted features is not only depending on the proper preprocessing of the input data but also on the suitability of the extraction algorithm for this problem. Possible approaches to address problems due to transparency requirements are the use of active appearance models in face recognition, smart segmentation, multi-camera solutions and tracking. In this paper an inventory of problems and possible solution will be give
Secure Mobile Crowdsensing with Deep Learning
In order to stimulate secure sensing for Internet of Things (IoT)
applications such as healthcare and traffic monitoring, mobile crowdsensing
(MCS) systems have to address security threats, such as jamming, spoofing and
faked sensing attacks, during both the sensing and the information exchange
processes in large-scale dynamic and heterogenous networks. In this article, we
investigate secure mobile crowdsensing and present how to use deep learning
(DL) methods such as stacked autoencoder (SAE), deep neural network (DNN), and
convolutional neural network (CNN) to improve the MCS security approaches
including authentication, privacy protection, faked sensing countermeasures,
intrusion detection and anti-jamming transmissions in MCS. We discuss the
performance gain of these DL-based approaches compared with traditional
security schemes and identify the challenges that need to be addressed to
implement them in practical MCS systems.Comment: 7 pages, 5 figure
Enhancing Trust in eAssessment - the TeSLA System Solution
Trust in eAssessment is an important factor for improving the quality of
online-education. A comprehensive model for trust based authentication for
eAssessment is being developed and tested within the score of the EU H2020
project TeSLA. The use of biometric verification technologies to authenticate
the identity and authorship claims of individual students in online-education
scenarios is a significant component of TeSLA. Technical Univerity of Sofia
(TUS) Bulgaria, a member of TeSLA consortium, participates in large-scale pilot
tests of the TeSLA system. The results of questionnaires to students and
teachers involved in the TUS pilot tests are analyzed and summarized in this
work. We also describe the TeSLA authentication and fraud-detection instruments
and their role for enhancing trust in eAssessment.Comment: Presented at the Conference on Technology Enhanced Assessment (TEA),
2018. 18 pages, 2 tables, 3 figure
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
Deep learning models have achieved high performance on many tasks, and thus
have been applied to many security-critical scenarios. For example, deep
learning-based face recognition systems have been used to authenticate users to
access many security-sensitive applications like payment apps. Such usages of
deep learning systems provide the adversaries with sufficient incentives to
perform attacks against these systems for their adversarial purposes. In this
work, we consider a new type of attacks, called backdoor attacks, where the
attacker's goal is to create a backdoor into a learning-based authentication
system, so that he can easily circumvent the system by leveraging the backdoor.
Specifically, the adversary aims at creating backdoor instances, so that the
victim learning system will be misled to classify the backdoor instances as a
target label specified by the adversary. In particular, we study backdoor
poisoning attacks, which achieve backdoor attacks using poisoning strategies.
Different from all existing work, our studied poisoning strategies can apply
under a very weak threat model: (1) the adversary has no knowledge of the model
and the training set used by the victim system; (2) the attacker is allowed to
inject only a small amount of poisoning samples; (3) the backdoor key is hard
to notice even by human beings to achieve stealthiness. We conduct evaluation
to demonstrate that a backdoor adversary can inject only around 50 poisoning
samples, while achieving an attack success rate of above 90%. We are also the
first work to show that a data poisoning attack can create physically
implementable backdoors without touching the training process. Our work
demonstrates that backdoor poisoning attacks pose real threats to a learning
system, and thus highlights the importance of further investigation and
proposing defense strategies against them
A Survey on Ear Biometrics
Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though, current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion; earprint forensics; ear symmetry; ear classification; and ear individuality. This paper provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers
WSMN: An optimized multipurpose blind watermarking in Shearlet domain using MLP and NSGA-II
Digital watermarking is a remarkable issue in the field of information
security to avoid the misuse of images in multimedia networks. Although access
to unauthorized persons can be prevented through cryptography, it cannot be
simultaneously used for copyright protection or content authentication with the
preservation of image integrity. Hence, this paper presents an optimized
multipurpose blind watermarking in Shearlet domain with the help of smart
algorithms including MLP and NSGA-II. In this method, four copies of the robust
copyright logo are embedded in the approximate coefficients of Shearlet by
using an effective quantization technique. Furthermore, an embedded random
sequence as a semi-fragile authentication mark is effectively extracted from
details by the neural network. Due to performing an effective optimization
algorithm for selecting optimum embedding thresholds, and also distinguishing
the texture of blocks, the imperceptibility and robustness have been preserved.
The experimental results reveal the superiority of the scheme with regard to
the quality of watermarked images and robustness against hybrid attacks over
other state-of-the-art schemes. The average PSNR and SSIM of the dual
watermarked images are 38 dB and 0.95, respectively; Besides, it can
effectively extract the copyright logo and locates forgery regions under severe
attacks with satisfactory accuracy
Offline Signature-Based Fuzzy Vault (OSFV: Review and New Results
An offline signature-based fuzzy vault (OSFV) is a bio-cryptographic
implementation that uses handwritten signature images as biometrics instead of
traditional passwords to secure private cryptographic keys. Having a reliable
OSFV implementation is the first step towards automating financial and legal
authentication processes, as it provides greater security of confidential
documents by means of the embedded handwritten signatures. The authors have
recently proposed the first OSFV implementation which is reviewed in this
paper. In this system, a machine learning approach based on the dissimilarity
representation concept is employed to select a reliable feature representation
adapted for the fuzzy vault scheme. Some variants of this system are proposed
for enhanced accuracy and security. In particular, a new method that adapts
user key size is presented. Performance of proposed methods are compared using
the Brazilian PUCPR and GPDS signature databases and results indicate that the
key-size adaptation method achieves a good compromise between security and
accuracy. While average system entropy is increased from 45-bits to about
51-bits, the AER (average error rate) is decreased by about 21%.Comment: This paper has been submitted to The 2014 IEEE Symposium on
Computational Intelligence in Biometrics and Identity Management (CIBIM
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