29 research outputs found
Reliable Face Morphing Attack Detection in On-The-Fly Border Control Scenario with Variation in Image Resolution and Capture Distance
Face Recognition Systems (FRS) are vulnerable to various attacks performed
directly and indirectly. Among these attacks, face morphing attacks are highly
potential in deceiving automatic FRS and human observers and indicate a severe
security threat, especially in the border control scenario. This work presents
a face morphing attack detection, especially in the On-The-Fly (OTF) Automatic
Border Control (ABC) scenario. We present a novel Differential-MAD (D-MAD)
algorithm based on the spherical interpolation and hierarchical fusion of deep
features computed from six different pre-trained deep Convolutional Neural
Networks (CNNs). Extensive experiments are carried out on the newly generated
face morphing dataset (SCFace-Morph) based on the publicly available SCFace
dataset by considering the real-life scenario of Automatic Border Control (ABC)
gates. Experimental protocols are designed to benchmark the proposed and
state-of-the-art (SOTA) D-MAD techniques for different camera resolutions and
capture distances. Obtained results have indicated the superior performance of
the proposed D-MAD method compared to the existing methods.Comment: The paper is accepted at the International Joint Conference on
Biometrics (IJCB) 202
Deep Composite Face Image Attacks: Generation, Vulnerability and Detection
Face manipulation attacks have drawn the attention of biometric researchers
because of their vulnerability to Face Recognition Systems (FRS). This paper
proposes a novel scheme to generate Composite Face Image Attacks (CFIA) based
on the Generative Adversarial Networks (GANs). Given the face images from
contributory data subjects, the proposed CFIA method will independently
generate the segmented facial attributes, then blend them using transparent
masks to generate the CFIA samples. { The primary motivation for CFIA is to
utilize deep learning to generate facial attribute-based composite attacks,
which has been explored relatively less in the current literature.} We generate
different combinations of facial attributes resulting in unique CFIA
samples for each pair of contributory data subjects. Extensive experiments are
carried out on our newly generated CFIA dataset consisting of 1000 unique
identities with 2000 bona fide samples and 14000 CFIA samples, thus resulting
in an overall 16000 face image samples. We perform a sequence of experiments to
benchmark the vulnerability of CFIA to automatic FRS (based on both
deep-learning and commercial-off-the-shelf (COTS). We introduced a new metric
named Generalized Morphing Attack Potential (GMAP) to benchmark the
vulnerability effectively. Additional experiments are performed to compute the
perceptual quality of the generated CFIA samples. Finally, the CFIA detection
performance is presented using three different Face Morphing Attack Detection
(MAD) algorithms. The proposed CFIA method indicates good perceptual quality
based on the obtained results. Further, { FRS is vulnerable to CFIA} (much
higher than SOTA), making it difficult to detect by human observers and
automatic detection algorithms. Lastly, we performed experiments to detect the
CFIA samples using three different detection techniques automatically
3D Face Morphing Attacks: Generation, Vulnerability and Detection
Face Recognition systems (FRS) have been found to be vulnerable to morphing
attacks, where the morphed face image is generated by blending the face images
from contributory data subjects. This work presents a novel direction for
generating face-morphing attacks in 3D. To this extent, we introduced a novel
approach based on blending 3D face point clouds corresponding to contributory
data subjects. The proposed method generates 3D face morphing by projecting the
input 3D face point clouds onto depth maps and 2D color images, followed by
image blending and wrapping operations performed independently on the color
images and depth maps. We then back-projected the 2D morphing color map and the
depth map to the point cloud using the canonical (fixed) view. Given that the
generated 3D face morphing models will result in holes owing to a single
canonical view, we have proposed a new algorithm for hole filling that will
result in a high-quality 3D face morphing model. Extensive experiments were
conducted on the newly generated 3D face dataset comprising 675 3D scans
corresponding to 41 unique data subjects and a publicly available database
(Facescape) with 100 data subjects. Experiments were performed to benchmark the
vulnerability of the {proposed 3D morph-generation scheme against} automatic
2D, 3D FRS, and human observer analysis. We also presented a quantitative
assessment of the quality of the generated 3D face-morphing models using eight
different quality metrics. Finally, we propose three different 3D face Morphing
Attack Detection (3D-MAD) algorithms to benchmark the performance of 3D face
morphing attack detection techniques.Comment: The paper is accepted at IEEE Transactions on Biometrics, Behavior
and Identity Scienc
Sound-Print: Generalised Face Presentation Attack Detection using Deep Representation of Sound Echoes
Facial biometrics are widely deployed in smartphone-based applications
because of their usability and increased verification accuracy in unconstrained
scenarios. The evolving applications of smartphone-based facial recognition
have also increased Presentation Attacks (PAs), where an attacker can present a
Presentation Attack Instrument (PAI) to maliciously gain access to the
application. Because the materials used to generate PAI are not deterministic,
the detection of unknown presentation attacks is challenging. In this paper, we
present an acoustic echo-based face Presentation Attack Detection (PAD) on a
smartphone in which the PAs are detected based on the reflection profiles of
the transmitted signal. We propose a novel transmission signal based on the
wide pulse that allows us to model the background noise before transmitting the
signal and increase the Signal-to-Noise Ratio (SNR). The received signal
reflections were processed to remove background noise and accurately represent
reflection characteristics. The reflection profiles of the bona fide and PAs
are different owing to the different reflection characteristics of the human
skin and artefact materials. Extensive experiments are presented using the
newly collected Acoustic Sound Echo Dataset (ASED) with 4807 samples captured
from bona fide and four different types of PAIs, including print (two types),
display, and silicone face-mask attacks. The obtained results indicate the
robustness of the proposed method for detecting unknown face presentation
attacks.Comment: Accepted in IJCB 202
Detecting Finger-Vein Presentation Attacks Using 3D Shape & Diffuse Reflectance Decomposition
Despite the high biometric performance, finger-vein recognition systems are
vulnerable to presentation attacks (aka., spoofing attacks). In this paper, we
present a new and robust approach for detecting presentation attacks on
finger-vein biometric systems exploiting the 3D Shape (normal-map) and material
properties (diffuse-map) of the finger. Observing the normal-map and
diffuse-map exhibiting enhanced textural differences in comparison with the
original finger-vein image, especially in the presence of varying illumination
intensity, we propose to employ textural feature-descriptors on both of them
independently. The features are subsequently used to compute a separating
hyper-plane using Support Vector Machine (SVM) classifiers for the features
computed from normal-maps and diffuse-maps independently. Given the scores from
each classifier for normal-map and diffuse-map, we propose sum-rule based score
level fusion to make detection of such presentation attack more robust. To this
end, we construct a new database of finger-vein images acquired using a custom
capture device with three inbuilt illuminations and validate the applicability
of the proposed approach. The newly collected database consists of 936 images,
which corresponds to 468 bona fide images and 468 artefact images. We establish
the superiority of the proposed approach by benchmarking it with classical
textural feature-descriptor applied directly on finger-vein images. The
proposed approach outperforms the classical approaches by providing the Attack
Presentation Classification Error Rate (APCER) & Bona fide Presentation
Classification Error Rate (BPCER) of 0% compared to comparable traditional
methods.Comment: This work was accepted in The 15th International Conference on SIGNAL
IMAGE TECHNOLOGY & INTERNET BASED SYSTEMS, 201
Potential and Opportunities of Agroforestry Practices in Combating Land Degradation
Agroforestry an established practice for centuries is the deliberate combination of perennials with food crops and/or livestock either simultaneously or sequentially. Agroforestry systems are bio-diverse and are associated in numerous ways for combating desertification and mitigating climate change. Agroforestry practice is a possible way of reducing deforestation and forest degradation and can alleviate resource-use pressure on natural conservation areas. Among many other reasons responsible for climate change, our traditional approaches towards forest management have failed thereby giving way to a drastic climate change, which slowly but has indeed harbingered the cataclysmic future that awaits us if we do not act now. This paper thus acquaints the readers with the role of agroforestry in mitigating the soil erosion, rehabilitation of degraded lands, improving water conservation and replenishment of soil fertility. Besides, the role of agroforestry in improving the soil health and overall ecosystem has also been discussed. This paper furthermore, attempts to recognize the role that agroforestry can play in mitigating the repercussions of climate change apart from improving natural resource sustainability and future food security issues
Morphing Attack Detection -- Database, Evaluation Platform and Benchmarking
Morphing attacks have posed a severe threat to Face Recognition System (FRS).
Despite the number of advancements reported in recent works, we note serious
open issues such as independent benchmarking, generalizability challenges and
considerations to age, gender, ethnicity that are inadequately addressed.
Morphing Attack Detection (MAD) algorithms often are prone to generalization
challenges as they are database dependent. The existing databases, mostly of
semi-public nature, lack in diversity in terms of ethnicity, various morphing
process and post-processing pipelines. Further, they do not reflect a realistic
operational scenario for Automated Border Control (ABC) and do not provide a
basis to test MAD on unseen data, in order to benchmark the robustness of
algorithms. In this work, we present a new sequestered dataset for facilitating
the advancements of MAD where the algorithms can be tested on unseen data in an
effort to better generalize. The newly constructed dataset consists of facial
images from 150 subjects from various ethnicities, age-groups and both genders.
In order to challenge the existing MAD algorithms, the morphed images are with
careful subject pre-selection created from the contributing images, and further
post-processed to remove morphing artifacts. The images are also printed and
scanned to remove all digital cues and to simulate a realistic challenge for
MAD algorithms. Further, we present a new online evaluation platform to test
algorithms on sequestered data. With the platform we can benchmark the morph
detection performance and study the generalization ability. This work also
presents a detailed analysis on various subsets of sequestered data and
outlines open challenges for future directions in MAD research.Comment: This paper is a pre-print. The article is accepted for publication in
IEEE Transactions on Information Forensics and Security (TIFS
Effect of Statistics Anxiety on MBBS Students’ Statistics Knowledge Achievement
In this paper the effect of Statistics anxiety on the MBBS students Statistics achievement knowledge. The present study was descriptive (survey type) in its nature and quantitative by approach. A sample of 300 students was drawn using purposive sampling technique by online survey method. Pearson product-moment correlation coefficient (r) and simple linear regression were used as inferential statistics to analyze the data. A moderate inverse correlation was investigated between Statistics anxiety and MBBS students “knowledge achievement. Through simple linear regression, 40% variation was noted due to Statistics anxiety in MBBS students” knowledge achievement