Anti Spoofing Research Articles

Improved multiview biometric object detection for anti spoofing frauds

Improved multiview biometric object detection for anti spoofing frauds

Machine Learning Based Non Live Finger Print Detection

In recent days, liveness detection of finger print image has become very essential in finger print recognition systems because fake finger prints are used in lieu of real finger prints. Many machine learning(ML) techniques have been widely used for non live finger print image detection because these techniques provide high accurate identification and also cost effective. These techniques also enhance the accuracy of classification of real and spoof finger print images. In this article , literature review is done about machine learning (ML) and its algorithms used for the detection of non live finger print. The main objective of this article is to compare and analyse various ML techniques used for spoof detection. It also provides an overview of performance merits and limitations of ML algorithms used in non live finger print detection . Index Terms— Non live finger print, liveness detection , Machine learning, anti spoofing, spoof detection

An assessment of reprocessed GPS/MET observations spanning 1995–1997

Abstract. We have performed an analysis of reprocessed GPS/MET data spanning 1995–1997 generated by the COSMIC Data Analysis and Archive Center (CDAAC) in 2007. CDAAC developed modified dual-frequency processing methods for the encrypted data (anti-spoofing (AS)-on) during 1995–1997. We compared the CDAAC data set to the Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) reanalysis, separately for AS-on and AS-off, focusing on the altitude range 10–30 km. MERRA-2 did not assimilate GPS/MET data in the period 1995–1997. To gain insight into the CDAAC data set, we developed a single-frequency data set for GPS/MET, which is unaffected by the presence of encryption. We find excellent agreement between the more limited single-frequency data set and the CDAAC data set: the bias between these two data sets is consistently less than 0.25 % in refractivity, whether or not AS is on. Given the different techniques applied between the CDAAC and the new data set presented here (designated JPL), agreement suggests that the CDAAC AS-on processing and the single-frequency processing are not biased in an aggregate sense greater than 0.25 % in refractivity, which corresponds approximately to a temperature bias less than 0.5 K. Since the profiles contained in the new single-frequency data set are not a subset of the CDAAC profiles, the combination of the CDAAC data set, consisting of 9579 profiles, and the new single-frequency data set, consisting of 4729 profiles, yields a total number of 11 531 unique profiles from combining the JPL and CDAAC data sets. All numbers are after quality control has been applied by the respective processing activities.

A remote PPG based approach for heart rate estimation

Heart rate (HR) estimation is an essential physiological process in the field of biosignal analysis. Recently, remote Photoplethysmography (rPPG) is a pathbreaking development in this field wherein the PPG signal is extracted from non-contact face videos. This study, aiming at building a low cost rPPG system for both heart rate estimation and detecting real face, proposes a non-contact, automatic technique of heart rate measurement from video based on the signal processing techniques. The heart rate is remotely measured from a video recording of a person's face using the camera from a smartphone. At the same time, the heart rate extracted from the videos recorded by a smartphone camera is compared with the data obtained from the PPG optical absorption sensor and achieved a similar result. The system can estimate the heart rates from faces from single subject or multiple participants, and can be applied for anti spoofing face recognition.

A study on data augmentation in voice anti-spoofing

In this paper we perform an in depth study of how data augmentation techniques improve synthetic or spoofed audio detection. Specifically, we propose methods to deal with channel variability, different audio compressions, different bandwidths and unseen spoofing attacks. These challenges, have all been shown to significantly degrade the performance of audio based systems and anti spoofing systems. Our results are based on the ASVspoof 2021 challenge, in the Logical Access (LA) and Deep Fake (DF) categories. Our study is Data-Centric, meaning that the models are fixed and we significantly improve the results by manipulating the data. We introduce two forms of data augmentation - compression augmentation for the DF part, and compression and channel augmentation for the LA part. In addition, we introduce a double sided log spectrogram feature design that improves the results significantly by centering the sub-bands of interest, where the discriminating spoofing artifacts can be localized. Furthermore, a new type of online data augmentation, SpecAverage, is introduced. This method includes masking the audio features with their average value in order to improve generalization. Our best single system and fusion schemes both achieve state of the art performance in the DF category, with an EER of 15.46% and 14.27%, respectively. Our best system for the LA task reduced the best baseline EER by 50% and the min t-DCF by 16%. Our techniques to deal with spoofed data from a wide variety of distributions can be replicated and can help anti spoofing and speech based systems enhance their results.

Spoofed Facial Presentation Attack Detection by Multivariate Gradient Descriptor in Micro-Expression Region

Facial video presentation is a topic of interest in many security systems due to its non-intrusive nature. However, such systems are vulnerable to spoof attacks made by fake face videos and thereby gaining unauthorized access in the system. For a robust biometric system anti spoofing approaches like liveness detection ought to be implemented in order to counter the aforesaid print and replay attacks. This article proposes a novel approach of anti spoofing using Multivariate histogram of oriented gradients descriptor in the auto detected micro expression (μE) regions of human facial videos. Facial μE are very brief, spontaneous facial expressions that highlight the face of humans when they either unconsciously or deliberately conceal an emotion. The work emphasizes the variations in μE in fake and original video representation by a considerable amount and claims such a variance is a tool to combat against presentation attacks. In particular, the method automatically extracts the ROI of major changes in μE using the Multivariate orientation gradients parameter and thus proposes this descriptor as one of the most suitable tools to characterize the liveness. The entire implementation is carried out on the self-created Database for replay attacks. The result obtained is satisfactory and tested statistically significant.

Spoofed replay attack detection by Multidimensional Fourier transform on facial micro-expression regions

Facial replay attacks have been a topic of interest in recent past due to the vulnerability of intrusive nature in biometric security systems. In order to build a robust biometric system many safeguard approaches have already been developed by the researchers to nullify spoofing activities like print and replay attacks. This paper proposes a comprehensive study on the application of Multidimensional Fourier transform to combat replay attacks. Since the higher frequency in Multidimensional Fourier transform contains the major feature variations, liveness of a face is mostly reflected in the high frequency spectrum. The spontaneous facial expressions like micro-expression(μE) carries the detailed inner facial variations. In this novel approach a modified high frequency descriptor is used for proper discrimination between a live and fake facial video streams. The descriptor in particular works efficiently for a change in facial μE. Inclusion of noise along with the feature variation is trivial in higher frequency spectrum. The method, therefore, during the pre-processing phase not only extracts the video frames with major μE changes but also filters out frames carrying any abrupt expression change (macro expression) or spike noise. The selected frame sequence are thereafter fed into the multi dimensional Fourier plane in order to detect the liveness. The experiment is performed on the self created dataset and also being tested on standard play back attack dataset. The result obtained by the proposed anti spoofing approach is satisfactory and verified to be statistically significant.

Evaluation of Local Descriptors and Deep CNN Features for Face Anti Spoofing

Recently facial recognition Technology are being habitual for various access control requirements and spoof detection in such a system has drawn growing attention. In this paper, we represent by comparison analysis of different local descriptors and off the shelf deep networks for feature extractionLocal Binary Pattern (LBP), SIFT, Histogram of Oriented Gradients (HOG), Shallow CNN, VGG16 and Inception-ResnetV2 for face spoofing detection. Furthermore, we evaluated three Classifiers-Decision Tree, Artificial Neural Network (ANN) and Support Vector Machine (SVM) over the feature extracted through local descriptors and deep networks. The evaluation has been conducted using publicly available YALE face database containing real and fake facial images. Real dataset consists of 5121 entries and fake dataset has 7508 images. The analysis results demonstrate that the best prediction accuracy of real and spoof is obtained with Inception_ResnetV2 features when classified with ANN and about 96.23% accuracy is achieved

Space Weather and the Global Positioning System

Space Weather and the Global Positioning System

Single frequency processing of atmospheric radio occultations

Tracking of the radio signals broadcast by the Global Positioning System (GPS) satellites as they are occulted from a GPS receiver by the Earth's atmosphere can provide high resolution vertical profiles of atmospheric refractivity, temperature and water vapour. Most implementations of this radio occultation technique use two GPS frequencies to correct for ionospheric effects. However, during most soundings, one of the frequencies is degraded by the introduction of the so-called Anti-Spoofing (AS) encryption mode. A retrieval method is discussed in this work for periods when only one of the two frequency signals has good quality. This method uses only the frequency with higher signal-to-noise ratio. We illustrate the quality of the atmospheric profiles obtained from such single frequency retrievals using GPS/MET data from the periods where the AS was turned off and the two frequencies were available. The results enable us to ensure the quality of a climate record of thousands of radio occultations collected by GPS/MET during the period with AS encryption, and the data processing of future missions with similar constraints, like IOX, can be performed.

Optimum Semicodeless Carrier-Phase Tracking of L2

ABSTRACT: This paper discusses the performance of various codeless and semicodeless techniques used to obtain the carrier phase of the GPS L2 signal without knowledge of the encrypted Anti-Spoofing (AS) Y-code. Four known techniques have been published in the past: squaring, cross-correlation, P-code aided L2 squaring, and Z-tracking. All these techniques perform significantly worse than an ideal phase lock loop. Five new techniques are presented in this paper: (1) code-aided L2 Costas loop with W-bit integrate and dump arm filtering, (2) P-code aided L1, L2 cross-correlation, (3) soft decision Z-tracking, (4) optimum L2 demodulation motivated by maximum a posteriori (MAP) estimation theory, and (5) linear approximation of the MAP approach. The first three are modifications of existing techniques, with improved performance. Techniques 4 and 5 are optimum and near-optimum techniques based on statistical estimation theory, and provide the best performance in squaring loss and mean time to cycle slip.

Total Electron Content Obtained by Using the Global Positioning System

A procedure is introduced to obtain the balance of measurements of differential group delays and differential carrier phase lead characteristics, both phenomena being due to the ionospheric total electron content(TEC)along the ray path from a Global Positioning System (GPS) satellite. It has been demonstrated that utilizing the measurement of both pseudorange and carrier phase recorded by genetic GPS receivers, the precision of the vertical TEC derived when the anti-spoofing (AS)is on can be as good as that derived when it is off. Combining the data of a network of four GPS receivers, a TEC map is reconstructed which can be employed to examine the ionospheric latitude/longitude structure and dynamics in the Taiwan area.

Inflation of Long Valley Caldera from one year of continuous GPS observations

A permanent Global Positioning System receiver at Casa Diablo Hot Springs, Long Valley Caldera, California was installed in January, 1993, and has operated almost continuously since then. The data have been transmitted daily to the Jet Propulsion Laboratory for routine analysis with data from the Fiducial Laboratories for an International Natural sciences Network (FLINN) by the JPL FLINN analysis center. Results from these analyses have been used to interpret the on going deformation at Long Valley, with data excluded from periods when the antenna was covered under 2.5 meters of snow and from some periods when Anti Spoofing was enforced on the GPS signal. The remaining time series suggests that uplift of the resurgent dome of Long Valley Caldera during 1993 has been 2.5±1.1 cm/yr and horizontal motion has been 3.0±0.7 cm/yr at S53W in a no‐net‐rotation global reference frame, or 1.5±0.7 cm/yr at S14W relative to the Sierra Nevada block. These rates are consistent with uplift predicted from frequent horizontal strain measurements. Spectral analysis of the observations suggests that tidal forcing of the magma chamber is not a source of the variability in the 3 dimensional station location. These results suggest that remotely operated, continuously recording GPS receivers could prove to be a reliable tool for volcano monitoring throughout the world.

Public access to GPS - government duty, economic rationality or international philanthropy?

Selective Availability (SA) of GPS has become a contentious reality in recent time and Anti-Spoofing (AS) looks set to follow it; military interests being considered paramount. Whilst many GPS user...