2,708 research outputs found
Biometric presentation attack detection: beyond the visible spectrum
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 i i) an in-depth analysis of several state-of-theart
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
Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks
Despite the rapid advancement of unsupervised learning in visual
representation, it requires training on large-scale datasets that demand costly
data collection, and pose additional challenges due to concerns regarding data
privacy. Recently, synthetic images generated by text-to-image diffusion
models, have shown great potential for benefiting image recognition. Although
promising, there has been inadequate exploration dedicated to unsupervised
learning on diffusion-generated images. To address this, we start by uncovering
that diffusion models' cross-attention layers inherently provide
annotation-free attention masks aligned with corresponding text inputs on
generated images. We then investigate the problems of three prevalent
unsupervised learning techniques ( i.e., contrastive learning, masked modeling,
and vision-language pretraining) and introduce customized solutions by fully
exploiting the aforementioned free attention masks. Our approach is validated
through extensive experiments that show consistent improvements in baseline
models across various downstream tasks, including image classification,
detection, segmentation, and image-text retrieval. By utilizing our method, it
is possible to close the performance gap between unsupervised pretraining on
synthetic data and real-world scenarios
Mixing Biometric Data For Generating Joint Identities and Preserving Privacy
Biometrics is the science of automatically recognizing individuals by utilizing biological traits such as fingerprints, face, iris and voice. A classical biometric system digitizes the human body and uses this digitized identity for human recognition. In this work, we introduce the concept of mixing biometrics. Mixing biometrics refers to the process of generating a new biometric image by fusing images of different fingers, different faces, or different irises. The resultant mixed image can be used directly in the feature extraction and matching stages of an existing biometric system. In this regard, we design and systematically evaluate novel methods for generating mixed images for the fingerprint, iris and face modalities. Further, we extend the concept of mixing to accommodate two distinct modalities of an individual, viz., fingerprint and iris. The utility of mixing biometrics is demonstrated in two different applications. The first application deals with the issue of generating a joint digital identity. A joint identity inherits its uniqueness from two or more individuals and can be used in scenarios such as joint bank accounts or two-man rule systems. The second application deals with the issue of biometric privacy, where the concept of mixing is used for de-identifying or obscuring biometric images and for generating cancelable biometrics. Extensive experimental analysis suggests that the concept of biometric mixing has several benefits and can be easily incorporated into existing biometric systems
Mobile app with steganography functionalities
[Abstract]: Steganography is the practice of hiding information within other data, such as images, audios,
videos, etc. In this research, we consider applying this useful technique to create a mobile
application that lets users conceal their own secret data inside other media formats, send that
encoded data to other users, and even perform analysis to images that may have been under
a steganography attack.
For image steganography, lossless compression formats employ Least Significant Bit (LSB)
encoding within Red Green Blue (RGB) pixel values. Reciprocally, lossy compression formats,
such as JPEG, utilize data concealment in the frequency domain by altering the quantized
matrices of the files.
Video steganography follows two similar methods. In lossless video formats that permit
compression, the LSB approach is applied to the RGB pixel values of individual frames.
Meanwhile, in lossy High Efficient Video Coding (HEVC) formats, a displaced bit modification
technique is used with the YUV components.[Resumo]: A esteganografía é a práctica de ocultar determinada información dentro doutros datos,
como imaxes, audio, vídeos, etc. Neste proxecto pretendemos aplicar esta técnica como visión
para crear unha aplicación móbil que permita aos usuarios ocultar os seus propios datos
secretos dentro doutros formatos multimedia, enviar eses datos cifrados a outros usuarios e
mesmo realizar análises de imaxes que puidesen ter sido comprometidas por un ataque esteganográfico.
Para a esteganografía de imaxes, os formatos con compresión sen perdas empregan a
codificación Least Significant Bit (LSB) dentro dos valores Red Green Blue (RGB) dos seus
píxeles. Por outra banda, os formatos de compresión con perdas, como JPEG, usan a ocultación
de datos no dominio de frecuencia modificando as matrices cuantificadas dos ficheiros.
A esteganografía de vídeo segue dous métodos similares. En formatos de vídeo sen perdas,
o método LSB aplícase aos valores RGB de píxeles individuais de cadros. En cambio, nos
formatos High Efficient Video Coding (HEVC) con compresión con perdas, úsase unha técnica
de cambio de bits nos compoñentes YUV.Traballo fin de grao (UDC.FIC). Enxeñaría Informática. Curso 2022/202
Web Tracking: Mechanisms, Implications, and Defenses
This articles surveys the existing literature on the methods currently used
by web services to track the user online as well as their purposes,
implications, and possible user's defenses. A significant majority of reviewed
articles and web resources are from years 2012-2014. Privacy seems to be the
Achilles' heel of today's web. Web services make continuous efforts to obtain
as much information as they can about the things we search, the sites we visit,
the people with who we contact, and the products we buy. Tracking is usually
performed for commercial purposes. We present 5 main groups of methods used for
user tracking, which are based on sessions, client storage, client cache,
fingerprinting, or yet other approaches. A special focus is placed on
mechanisms that use web caches, operational caches, and fingerprinting, as they
are usually very rich in terms of using various creative methodologies. We also
show how the users can be identified on the web and associated with their real
names, e-mail addresses, phone numbers, or even street addresses. We show why
tracking is being used and its possible implications for the users (price
discrimination, assessing financial credibility, determining insurance
coverage, government surveillance, and identity theft). For each of the
tracking methods, we present possible defenses. Apart from describing the
methods and tools used for keeping the personal data away from being tracked,
we also present several tools that were used for research purposes - their main
goal is to discover how and by which entity the users are being tracked on
their desktop computers or smartphones, provide this information to the users,
and visualize it in an accessible and easy to follow way. Finally, we present
the currently proposed future approaches to track the user and show that they
can potentially pose significant threats to the users' privacy.Comment: 29 pages, 212 reference
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