The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to biometric systems. Among them, presentation attacks (PAs, i.e., attempts to log into the system with a fake biometric characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. In this context, we present a novel fingerprint presentation attack detection (PAD) scheme based on i) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and ii) an in-depth analysis of several state-of-the art techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.35% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks).
The encryption mechanism is a digital coding system dedicated to preserving the confidentiality and integrity of data. It is used for encoding plain text data into a protected and unreadable format.
Cloud computing notes unit I as per RGPV syllabusNANDINI SHARMA
Cloud Computing
Historical development ,Vision of Cloud Computing, Characteristics of cloud
computing as per NIST , Cloud computing reference model ,Cloud computing environments,
Cloud services requirements, Cloud and dynamic infrastructure, Cloud Adoption and rudiments
.Overview of cloud applications: ECG Analysis in the cloud, Protein structure prediction, Gene
Expression Data Analysis ,Satellite Image Processing ,CRM and ERP ,Social networking .
The encryption mechanism is a digital coding system dedicated to preserving the confidentiality and integrity of data. It is used for encoding plain text data into a protected and unreadable format.
Cloud computing notes unit I as per RGPV syllabusNANDINI SHARMA
Cloud Computing
Historical development ,Vision of Cloud Computing, Characteristics of cloud
computing as per NIST , Cloud computing reference model ,Cloud computing environments,
Cloud services requirements, Cloud and dynamic infrastructure, Cloud Adoption and rudiments
.Overview of cloud applications: ECG Analysis in the cloud, Protein structure prediction, Gene
Expression Data Analysis ,Satellite Image Processing ,CRM and ERP ,Social networking .
Cloud Forensics...this presentation shows you the current state of progress and challenges that stand today in the world of CLOUD FORENSICS.Based on lots of Google search and whites by Josiah Dykstra and Alan Sherman.The presentation builds right from basics and compares the conflicting requirements between traditional and Clod Forensics.
Computer Security and Intrusion Detection(IDS/IPS)LJ PROJECTS
This ppt explain you various type of possible attack, security property, Traffic Analysis, Security mechanism Intrusion detection system, vulnerability, Attack framework etc.
INTRODUCTION TO COMPUTER FORENSICS
Introduction to Traditional Computer Crime, Traditional problems associated with Computer Crime. Introduction to Identity Theft & Identity Fraud. Types of CF techniques – Incident and incident response methodology – Forensic duplication and investigation. Preparation for IR: Creating response tool kit and IR team. – Forensics Technology and Systems – Understanding Computer Investigation – Data Acquisition.
Trust models for Grid security environment – Authentication and Authorization methods – Grid security infrastructure – Cloud Infrastructure security: network, host and application level – aspects of data security, provider data and its security, Identity and access management architecture, IAM practices in the cloud, SaaS, PaaS, IaaS availability in the cloud, Key privacy issues in the cloud.
Network Security protects your network and data from breaches, intrusions and other threats. View this presentation now to understand what is network security and the types of network security.
Happy learning!!
Robust Malware Detection using Residual Attention NetworkShamika Ganesan
In this paper, we explore the use of an attention based mechanism known as Residual Attention for malware detection and compare this with existing CNN based methods and conventional Machine Learning algorithms with the help of GIST features. The proposed method outperformed traditional malware detection methods which use Machine Learning and CNN based Deep Learning algorithms, by demonstrating an accuracy of 99.25%.
This paper has been accepted in the International Conference of Consumer Electronics (ICCE 2021).
Cloud Forensics...this presentation shows you the current state of progress and challenges that stand today in the world of CLOUD FORENSICS.Based on lots of Google search and whites by Josiah Dykstra and Alan Sherman.The presentation builds right from basics and compares the conflicting requirements between traditional and Clod Forensics.
Computer Security and Intrusion Detection(IDS/IPS)LJ PROJECTS
This ppt explain you various type of possible attack, security property, Traffic Analysis, Security mechanism Intrusion detection system, vulnerability, Attack framework etc.
INTRODUCTION TO COMPUTER FORENSICS
Introduction to Traditional Computer Crime, Traditional problems associated with Computer Crime. Introduction to Identity Theft & Identity Fraud. Types of CF techniques – Incident and incident response methodology – Forensic duplication and investigation. Preparation for IR: Creating response tool kit and IR team. – Forensics Technology and Systems – Understanding Computer Investigation – Data Acquisition.
Trust models for Grid security environment – Authentication and Authorization methods – Grid security infrastructure – Cloud Infrastructure security: network, host and application level – aspects of data security, provider data and its security, Identity and access management architecture, IAM practices in the cloud, SaaS, PaaS, IaaS availability in the cloud, Key privacy issues in the cloud.
Network Security protects your network and data from breaches, intrusions and other threats. View this presentation now to understand what is network security and the types of network security.
Happy learning!!
Robust Malware Detection using Residual Attention NetworkShamika Ganesan
In this paper, we explore the use of an attention based mechanism known as Residual Attention for malware detection and compare this with existing CNN based methods and conventional Machine Learning algorithms with the help of GIST features. The proposed method outperformed traditional malware detection methods which use Machine Learning and CNN based Deep Learning algorithms, by demonstrating an accuracy of 99.25%.
This paper has been accepted in the International Conference of Consumer Electronics (ICCE 2021).
Leading water utility company in USA was facing a challenge to improve pipeline inspection process to reduce human errors and manual inspection time.Pipeline Anomaly Detection automates the process of identification of defects in pipeline videos, by a camera which notes the observations and lastly it generates the report.
A hybrid approach for face recognition using a convolutional neural network c...IAESIJAI
Facial recognition technology has been used in many fields such as security,
biometric identification, robotics, video surveillance, health, and commerce
due to its ease of implementation and minimal data processing time.
However, this technology is influenced by the presence of variations such as
pose, lighting, or occlusion. In this paper, we propose a new approach to
improve the accuracy rate of face recognition in the presence of variation or
occlusion, by combining feature extraction with a histogram of oriented
gradient (HOG), scale invariant feature transform (SIFT), Gabor, and the
Canny contour detector techniques, as well as a convolutional neural
network (CNN) architecture, tested with several combinations of the
activation function used (Softmax and Segmoïd) and the optimization
algorithm used during training (adam, Adamax, RMSprop, and stochastic
gradient descent (SGD)). For this, a preprocessing was performed on two
databases of our database of faces (ORL) and Sheffield faces used, then we
perform a feature extraction operation with the mentioned techniques and
then pass them to our used CNN architecture. The results of our simulations
show a high performance of the SIFT+CNN combination, in the case of the
presence of variations with an accuracy rate up to 100%.
RECOGNITION OF CDNA MICROARRAY IMAGE USING FEEDFORWARD ARTIFICIAL NEURAL NETWORKijaia
The complementary DNA (cDNA) sequence considered the magic biometric technique for personal identification. Microarray image processing used for the concurrent genes identification. In this paper, we present a new method for cDNA recognition based on the artificial neural network (ANN). We have segmented the location of the spots in a cDNA microarray. Thus, a precise localization and segmenting of a spot are essential to obtain a more exact intensity measurement, leading to a more accurate gene expression measurement. The segmented cDNA microarray image resized and used as an input for the
proposed artificial neural network. For matching and recognition, we have trained the artificial neural
network. Recognition results are given for the galleries of cDNA sequences . The numerical results show
that, the proposed matching technique is an effective in the cDNA sequences process. The experimental
results of our matching approach using different databases shows that, the proposed technique is an effective matching performance.
A Novel Biometric Approach for Authentication In Pervasive Computing Environm...aciijournal
The paradigm of embedding computing devices in our surrounding environment has gained more interest
in recent days. Along with contemporary technology comes challenges, the most important being the
security and privacy aspect. Keeping the aspect of compactness and memory constraints of pervasive
devices in mind, the biometric techniques proposed for identification should be robust and dynamic. In this
work, we propose an emerging scheme that is based on few exclusive human traits and characteristics
termed as ocular biometrics, promising utmost security and reliability. Complex iris recognition and
retinal scanning algorithms have been discussed which promises achievement of accurate results. The
performance and vast applications of these algorithms on pervasive computing devices is also addressed.
A Novel Biometric Approach for Authentication In Pervasive Computing Environm...aciijournal
The paradigm of embedding computing devices in our
surrounding environment has gained more interest
in recent days. Along with contemporary technology
comes challenges, the most important being the
security and privacy aspect. Keeping the aspect of
compactness and memory constraints of pervasive
devices in mind, the biometric techniques proposed
for identification should be robust and dynamic. In
this
work, we propose an emerging scheme that is based on few exclusive human traits and characteristics termed as ocular biometrics, promising utmost security and reliability. Complex iris recognition and retinal scanning algorithms have been discussed whi
ch promises achievement of accurate results. The
performance and vast applications of these algorithms on pervasive computing devices is also addressed.
A NOVEL BIOMETRIC APPROACH FOR AUTHENTICATION IN PERVASIVE COMPUTING ENVIRONM...aciijournal
The paradigm of embedding computing devices in our surrounding environment has gained more interest
in recent days. Along with contemporary technology comes challenges, the most important being the
security and privacy aspect. Keeping the aspect of compactness and memory constraints of pervasive
devices in mind, the biometric techniques proposed for identification should be robust and dynamic. In this
work, we propose an emerging scheme that is based on few exclusive human traits and characteristics
termed as ocular biometrics, promising utmost security and reliability. Complex iris recognition and
retinal scanning algorithms have been discussed which promises achievement of accurate results. The
performance and vast applications of these algorithms on pervasive computing devices is also addressed.
Advanced Computational Intelligence: An International Journal (ACII)aciijournal
The paradigm of embedding computing devices in our surrounding environment has gained more interest
in recent days. Along with contemporary technology comes challenges, the most important being the
security and privacy aspect. Keeping the aspect of compactness and memory constraints of pervasive
devices in mind, the biometric techniques proposed for identification should be robust and dynamic. In this
work, we propose an emerging scheme that is based on few exclusive human traits and characteristics
termed as ocular biometrics, promising utmost security and reliability. Complex iris recognition and
retinal scanning algorithms have been discussed which promises achievement of accurate results. The
performance and vast applications of these algorithms on pervasive computing devices is also addressed.
Image processing analysis of sigmoidal Hadamard wavelet with PCA to detect hi...TELKOMNIKA JOURNAL
Innovative tactics are employed by terrorists to conceal weapons and explosives to perpetrate violent attacks, accounting for the deaths of millions of lives every year and contributing to huge economic losses to the global society. Achieving a high threat detection rate during an inspection of crowds to recognize and detect threat elements from a secure distance is the motivation for the development of intelligent image data analysis from a machine learning perspective. A method proposed to reduce the image dimensions with support vector, linearity and orthogonal. The functionality of CWD is contingent upon the plenary characterization of fusion data from multiple image sensors. The proposed method combines multiple sensors by hybrid fusion of sigmoidal Hadamard wavelet transform and PCA basis functions. Weapon recognition and the detection system, using Image segmentation and K means support vector machine A classifier is an autonomous process for the recognition of threat weapons regardless of make, variety, shape, or position on the suspect’s body despite concealment.
Deep hypersphere embedding for real-time face recognitionTELKOMNIKA JOURNAL
With the advancement of human-computer interaction capabilities of robots, computer vision surveillance systems involving security yields a large impact in the research industry by helping in digitalization of certain security processes. Recognizing a face in the computer vision involves identification and classification of which faces belongs to the same person by means of comparing face embedding vectors. In an organization that has a large and diverse labelled dataset on a large number of epoch, oftentimes, creates a training difficulties involving incompatibility in different versions of face embedding that leads to poor face recognition accuracy. In this paper, we will design and implement robotic vision security surveillance system incorporating hybrid combination of MTCNN for face detection, and FaceNet as the unified embedding for face recognition and clustering.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
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• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
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• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
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Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
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Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
3. INTRODUCTION
Biometric recognition systems is based on the individuals’ biological
(e.g., iris or fingerprint) or behavioural (e.g., signature or voice)
characteristics.
In spite of their numerous advantages, biometric systems are
vulnerable to external attacks as any other security-related
technology. The biometric capture device is probably the most
exposed one.
One can simply present the capture device with a presentation attack
instrument (PAI), such as a gummy finger or a fingerprint overlay, in
order to interfere with its intended behaviour. These attacks are
known as presentation attacks (PAs).
3
4. The initial approaches to PAD were based on the so-called
handcrafted features, such as texture descriptors or motion
analysis.
However, deep learning (DL) has become a thriving topic in the
biometric recognition in general.
More specifically, convolutional neural networks (CNNs) and deep
belief networks (DBNs) have been used for fingerprint PAD
purposes.
Selected SWIR wavelengths are used to discriminate skin from
other materials.
Human skin shows characteristic remission properties for
multispectral SWIR wavelengths, which are independent of a
person’s age, gender or skin type.
4
5. DEFINITION
Bona fide Presentation: A normal or genuine presentation.
Presentation attack (PA): An attack carried out on the capture device to
either conceal your identity or impersonate someone else.
Presentation attack instrument (PAI): biometric characteristic or object
used in a presentation attack.
Attack Presentation Classification Error Rate (APCER): Proportion of attack
presentations using the same PAI species incorrectly classified as bona fide
presentations in a specific scenario.
Bona fide Presentation Classification Error Rate (BPCER):Proportion of bona
fide presentations incorrectly classified as presentation attacks in a
specific scenario.
5
6. RELATED WORKS
Key works on fingerprint PAD for both non-conventional sensors and conventional sensors.
Non-Conventional Fingerprint Sensor
6
8. PRESENTATION ATTACK DETECTION METHODOLOGY:
HARDWARE
The finger SWIR capture device
contain a camera and SWIR sensor.
Captures 64 × 64 px images, with a
25 mm fixed focal length lens
optimised for wavelengths within
900 – 1700 nm.
Region of interest(ROI) is extracted
from background of size 18 × 58 px
8
10. PRESENTATION ATTACK DETECTION METHODOLOGY:
SOFTWARE
Two approaches are: i) handcrafted features
ii) deep learning features
A) Handcrafted features
This method is builds upon the raw spectral signature(ss) of the pixels
across all four wavelengths in order to capture bona fide presentation and
PAI materials.
SWIR sensor provides raw spectral signature(ss) as:
𝒔𝒔 𝒙, 𝒚 = {i1(x,y), … … … iN(x,y)}
iN(x,y) represents the intensity value of pixels for n-th wavelength.
10
11. However, this original representation of the sensor is vulnerable to
illumination changes.
Only the differences among wavelengths will be used as our set of
handcrafted features. Therefore, for each pixel, the final normalised
difference feature vector d(x, y) is computed as follows:
d(x, y) ={d [ia,ib](x, y)} 1≤a<b≤N
For each pixel with coordinates (x,y), the normalised difference feature
vector d(x,y) is used to classify as skin vs. non-skin with a Support
Vector Machine (SVM) classifier.
11
12. B) Deep Learning Features
1. Training CNN Models From Scratch:
The first approach is focused on training residual CNNs from scratch.
The characteristics of this network is the insertion of shortcut
connections every few stacked layers, converting the plain network into
its residual version.
The residual connections allow the use of deeper neural network
architectures and at the same time decrease their training time
significantly.
12
13. 2. Adapting Pre-Trained CNN Models
The second approach evaluates the potential of state-of-the-art pre-trained
models for fingerprint PAD.
Here the classifier is replaced and retrained and the weights of the last
convolutional layers is adapted.
MobileNet and VGG19 network architectures pre-trained using the
ImageNet database are proposed to use.
MobileNet network is based on depthwise separable convolutions, which
factorize a standard convolution into: i) a depthwise convolution, and ii) a
1×1 convolution called pointwise convolution.
VGG19 network is one of the most popular network architecture, providing
very good results due to its simplicity.
13
14. EXPERIMENTAL FRAMEWORK
A. DATABASE
Data were collected in two different stages and comprises both bona
fide and PA samples.
For the bona fide samples, a total of 163 subjects participated during
the first stage. For each of them, all 5 fingers of the right hand were
captured. For the second stage, there were a total of 399 subjects.
For the PA samples, there are a total of 35 different PAI species.
14
15. B. EXPERIMENTAL PROTOCOL
The main goal behind the experimental protocol design is to
analyze and prove the soundness of our proposed fingerprint PAD
approach in a realistic scenario.
For the development of our proposed fingerprint PAD methods,
both training and validation datasets are used in order to train the
weights of the systems and select the optimal network
architectures.
15
17. Spectral signature pixel-wise approach has achieved a 12.61% D-EER.
It is not possible to get an APCER ≤ 2%, and for APCER ≈ 5%, the BPCER is
over 20% .
For the case of training end-to-end residual CNN models from scratch, the
best result obtained is a 2.25% D-EER. This result outperforms the
handcrafted feature approach by an 82%.
Low APCERs below 1% can be achieved for BPCERs below 8%, thereby
overcoming the main drawback of the handcrafted feature approach.
Three CNN approaches have been fused in two by two basis.
That yields convenient systems (i.e., low BPCER) even for highly secure (i.e.,
very low APCER) scenarios.
Our proposed fingerprint PAD system has achieved a final 1.35% D-EER.
Furthermore, other operating points yield a BPCER of 2% for APCER ≤ 0.5%,
and an APCER ≈ 7% for BPCER=0.1%.
17
18. CONCLUSION
A fingerprint PAD scheme based on i) a new capture device able to
acquire images within the short wave infrared (SWIR) spectrum, and
ii) state-of-the-art deep learning techniques.
The best performance was reached for the fusion of two end-to-end
CNNs: the residual CNN trained from scratch and the adapted VGG19
pre-trained model. A D-EER of 1.35% was obtained.
These results clearly outperform those achieved with the handcrafted
features, which yielded a D-EER over 12%.
The use of SWIR images in combination with state-of-the-art CNNs
offers a reliable and efficient solution to the threat posed by presentation
attacks.
18
19. Reference
J. Kolberg, M. Gomez-Barrero, S. Venkatesh, R. Raghavendra, and C. Busch,
“Presentation attack detection for finger recognition,” in Handbook of Vascular
Biometrics, S. Marcel, A. Uhl, R. Veldhuis, and C. Busch, Eds. Springer, 2019.
E. Park, W. Kim, Q. Li, J. Kim, and H. Kim, “Fingerprint liveness detection using
CNN features of random sample patches,” in Proc. Int. Conf. Biometrics Special
Interest Group (BIOSIG), Sep. 2016.
R. Tolosana, M. Gomez-Barrero, J. Kolberg, A. Morales, C. Busch, and J. Ortega,
“Towards fingerprint presentation attack detection based on convolutional
neural networks and short wave infrared imaging,” in Proc. Int. Conf.
Biometrics Special Interest Group (BIOSIG), Sep. 2018.
19
The "handcrafted features" were commonly used with "traditional" machine learning approaches for object recognition and computer vision like Support Vector Machines.
understanding of how an object moves within an environment.
1.Not many details about the acquired database or the experimental protocol are available.
2. the spectroscopic properties of living against the cadaver fingers were analyzed using four wavelengths. However, no PAIs were analysed in their work.
3. Finger vein image over same database was analyzed with help of gaussian pyramid and LBP.
1.CNN optimization was used , best accuracy was achieved
2. 2 Pre trained CNN cafenet and googlenet, performance was compared with Siamese ntw.
ROI: Different approach to optimize CNN model, output were classified by SVM, no data agumantation required
Patch: deep boltzman machine which learn more complex samples.
Instead of using deep n/ws 10 different handcrafted descriptors were used, which were fed to self developed deep ntw for final fusion.
Hamamatsu G11097-0606S InGaAs area image sensor,
4 wavelength 1200nm ,1300nm 1450nm,1550nm
Fingerprint verification can be done with 1.3 MP camera and VIS NIS lens.
4 wavelength sample acquired from 2 bonafide and 3 PAIs
Playdoh finger show some similarities wrt BP which makes PAD task harder.
Change are different making it easier to discriminate from BP
each individual score si generated by the individual PAD algorithms
Due to having the finger slot open to outer world
These ntw have outperformed the traditional ntw in many different datasets such as ImageNet for both image classification and object detection.
Batch normalization is applied r8 after each convolution
first layers of the CNN extract more general features related to directional edges and colours, whereas the last layers of the network are in charge of extracting more abstract features related to the specific task
People from different gender, ethinicity,and age were considerd
PAI species were categorized into 8 groups.
1.for APCER ≤ 0.5%, the corresponding BPCER values for the fused systems (solid lines) are significantly lower than those of the individual networks (dashed lines): close to 2%
2.for low BPCER ≤1%, the best APCER (≤10%) is achieved for either the residual CNN alone (dashed orange) or its fusion with the VGG19 (solid dark blue).