1,226 research outputs found
Maximum Entropy Binary Encoding for Face Template Protection
In this paper we present a framework for secure identification using deep
neural networks, and apply it to the task of template protection for face
authentication. We use deep convolutional neural networks (CNNs) to learn a
mapping from face images to maximum entropy binary (MEB) codes. The mapping is
robust enough to tackle the problem of exact matching, yielding the same code
for new samples of a user as the code assigned during training. These codes are
then hashed using any hash function that follows the random oracle model (like
SHA-512) to generate protected face templates (similar to text based password
protection). The algorithm makes no unrealistic assumptions and offers high
template security, cancelability, and state-of-the-art matching performance.
The efficacy of the approach is shown on CMU-PIE, Extended Yale B, and
Multi-PIE face databases. We achieve high (~95%) genuine accept rates (GAR) at
zero false accept rate (FAR) with up to 1024 bits of template security.Comment: arXiv admin note: text overlap with arXiv:1506.0434
Deep Secure Encoding: An Application to Face Recognition
In this paper we present Deep Secure Encoding: a framework for secure
classification using deep neural networks, and apply it to the task of
biometric template protection for faces. Using deep convolutional neural
networks (CNNs), we learn a robust mapping of face classes to high entropy
secure codes. These secure codes are then hashed using standard hash functions
like SHA-256 to generate secure face templates. The efficacy of the approach is
shown on two face databases, namely, CMU-PIE and Extended Yale B, where we
achieve state of the art matching performance, along with cancelability and
high security with no unrealistic assumptions. Furthermore, the scheme can work
in both identification and verification modes
Reconstruction of Privacy-Sensitive Data from Protected Templates
In this paper, we address the problem of data reconstruction from
privacy-protected templates, based on recent concept of sparse ternary coding
with ambiguization (STCA). The STCA is a generalization of randomization
techniques which includes random projections, lossy quantization, and addition
of ambiguization noise to satisfy the privacy-utility trade-off requirements.
The theoretical privacy-preserving properties of STCA have been validated on
synthetic data. However, the applicability of STCA to real data and potential
threats linked to reconstruction based on recent deep reconstruction algorithms
are still open problems. Our results demonstrate that STCA still achieves the
claimed theoretical performance when facing deep reconstruction attacks for the
synthetic i.i.d. data, while for real images special measures are required to
guarantee proper protection of the templates.Comment: accepted at ICIP 201
Fingerprints: Fixed Length Representation via Deep Networks and Domain Knowledge
We learn a discriminative fixed length feature representation of fingerprints
which stands in contrast to commonly used unordered, variable length sets of
minutiae points. To arrive at this fixed length representation, we embed
fingerprint domain knowledge into a multitask deep convolutional neural network
architecture. Empirical results, on two public-domain fingerprint databases
(NIST SD4 and FVC 2004 DB1) show that compared to minutiae representations,
extracted by two state-of-the-art commercial matchers (Verifinger v6.3 and
Innovatrics v2.0.3), our fixed-length representations provide (i) higher search
accuracy: Rank-1 accuracy of 97.9% vs. 97.3% on NIST SD4 against a gallery size
of 2000 and (ii) significantly faster, large scale search: 682,594 matches per
second vs. 22 matches per second for commercial matchers on an i5 3.3 GHz
processor with 8 GB of RAM
Privacy-Enabled Biometric Search
Biometrics have a long-held hope of replacing passwords by establishing a
non-repudiated identity and providing authentication with convenience.
Convenience drives consumers toward biometrics-based access management
solutions. Unlike passwords, biometrics cannot be script-injected; however,
biometric data is considered highly sensitive due to its personal nature and
unique association with users. Biometrics differ from passwords in that
compromised passwords may be reset. Compromised biometrics offer no such
relief. A compromised biometric offers unlimited risk in privacy (anyone can
view the biometric) and authentication (anyone may use the biometric).
Standards such as the Biometric Open Protocol Standard (BOPS) (IEEE 2410-2016)
provide a detailed mechanism to authenticate biometrics based on pre-enrolled
devices and a previous identity by storing the biometric in encrypted form.
This paper describes a biometric-agnostic approach that addresses the privacy
concerns of biometrics through the implementation of BOPS. Specifically, two
novel concepts are introduced. First, a biometric is applied to a neural
network to create a feature vector. This neural network alone can be used for
one-to-one matching (authentication), but would require a search in linear time
for the one-to-many case (identity lookup). The classifying algorithm described
in this paper addresses this concern by producing normalized floating-point
values for each feature vector. This allows authentication lookup to occur in
up to polynomial time, allowing for search in encrypted biometric databases
with speed, accuracy and privacy.Comment: 5 pages, 1 figur
Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound
In this work we propose a novel approach to perform segmentation by
leveraging the abstraction capabilities of convolutional neural networks
(CNNs). Our method is based on Hough voting, a strategy that allows for fully
automatic localisation and segmentation of the anatomies of interest. This
approach does not only use the CNN classification outcomes, but it also
implements voting by exploiting the features produced by the deepest portion of
the network. We show that this learning-based segmentation method is robust,
multi-region, flexible and can be easily adapted to different modalities. In
the attempt to show the capabilities and the behaviour of CNNs when they are
applied to medical image analysis, we perform a systematic study of the
performances of six different network architectures, conceived according to
state-of-the-art criteria, in various situations. We evaluate the impact of
both different amount of training data and different data dimensionality (2D,
2.5D and 3D) on the final results. We show results on both MRI and transcranial
US volumes depicting respectively 26 regions of the basal ganglia and the
midbrain
FDFNet : A Secure Cancelable Deep Finger Dorsal Template Generation Network Secured via. Bio-Hashing
Present world has already been consistently exploring the fine edges of
online and digital world by imposing multiple challenging problems/scenarios.
Similar to physical world, personal identity management is very crucial
in-order to provide any secure online system. Last decade has seen a lot of
work in this area using biometrics such as face, fingerprint, iris etc. Still
there exist several vulnerabilities and one should have to address the problem
of compromised biometrics much more seriously, since they cannot be modified
easily once compromised. In this work, we have proposed a secure cancelable
finger dorsal template generation network (learning domain specific features)
secured via. Bio-Hashing. Proposed system effectively protects the original
finger dorsal images by withdrawing compromised template and reassigning the
new one. A novel Finger-Dorsal Feature Extraction Net (FDFNet) has been
proposed for extracting the discriminative features. This network is
exclusively trained on trait specific features without using any kind of
pre-trained architecture. Later Bio-Hashing, a technique based on assigning a
tokenized random number to each user, has been used to hash the features
extracted from FDFNet. To test the performance of the proposed architecture, we
have tested it over two benchmark public finger knuckle datasets: PolyU FKP and
PolyU Contactless FKI. The experimental results shows the effectiveness of the
proposed system in terms of security and accuracy.Comment: Accepted in ISBA 2019: International Conference on Identity, Security
and Behavior Analysi
Multibiometric Secure System Based on Deep Learning
In this paper, we propose a secure multibiometric system that uses deep
neural networks and error-correction coding. We present a feature-level fusion
framework to generate a secure multibiometric template from each user's
multiple biometrics. Two fusion architectures, fully connected architecture and
bilinear architecture, are implemented to develop a robust multibiometric
shared representation. The shared representation is used to generate a
cancelable biometric template that involves the selection of a different set of
reliable and discriminative features for each user. This cancelable template is
a binary vector and is passed through an appropriate error-correcting decoder
to find a closest codeword and this codeword is hashed to generate the final
secure template. The efficacy of the proposed approach is shown using a
multimodal database where we achieve state-of-the-art matching performance,
along with cancelability and security.Comment: To be published in Proc. IEEE Global SIP 201
Resist : Reconstruction of irises from templates
Iris recognition systems transform an iris image into a feature vector. The
seminal pipeline segments an image into iris and non-iris pixels, normalizes
this region into a fixed-dimension rectangle, and extracts features which are
stored and called a template (Daugman, 2009). This template is stored on a
system. A future reading of an iris can be transformed and compared against
template vectors to determine or verify the identity of an individual. As
templates are often stored together, they are a valuable target to an attacker.
We show how to invert templates across a variety of iris recognition systems.
Our inversion is based on a convolutional neural network architecture we call
RESIST (REconStructing IriSes from Templates). We apply RESIST to a traditional
Gabor filter pipeline, to a DenseNet (Huang et al., CVPR 2017) feature
extractor, and to a DenseNet architecture that works without normalization.
Both DenseNet feature extractors are based on the recent ThirdEye recognition
system (Ahmad and Fuller, BTAS 2019). When training and testing using the
ND-0405 dataset, reconstructed images demonstrate a rank-1 accuracy of 100%,
76%, and 96% respectively for the three pipelines. The core of our approach is
similar to an autoencoder. To obtain high accuracy this core is integrated into
an adversarial network (Goodfellow et al., NeurIPS, 2014
An optimized system to solve text-based CAPTCHA
CAPTCHA(Completely Automated Public Turing test to Tell Computers and Humans
Apart) can be used to protect data from auto bots. Countless kinds of CAPTCHAs
are thus designed, while we most frequently utilize text-based scheme because
of most convenience and user-friendly way \cite{bursztein2011text}. Currently,
various types of CAPTCHAs need corresponding segmentation to identify single
character due to the numerous different segmentation ways. Our goal is to
defeat the CAPTCHA, thus firstly the CAPTCHAs need to be split into character
by character. There isn't a regular segmentation algorithm to obtain the
divided characters in all kinds of examples, which means that we have to treat
the segmentation individually. In this paper, we build a whole system to defeat
the CAPTCHAs as well as achieve state-of-the-art performance. In detail, we
present our self-adaptive algorithm to segment different kinds of characters
optimally, and then utilize both the existing methods and our own constructed
convolutional neural network as an extra classifier. Results are provided
showing how our system work well towards defeating these CAPTCHAs
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