262 research outputs found
A reliable order-statistics-based approximate nearest neighbor search algorithm
We propose a new algorithm for fast approximate nearest neighbor search based
on the properties of ordered vectors. Data vectors are classified based on the
index and sign of their largest components, thereby partitioning the space in a
number of cones centered in the origin. The query is itself classified, and the
search starts from the selected cone and proceeds to neighboring ones. Overall,
the proposed algorithm corresponds to locality sensitive hashing in the space
of directions, with hashing based on the order of components. Thanks to the
statistical features emerging through ordering, it deals very well with the
challenging case of unstructured data, and is a valuable building block for
more complex techniques dealing with structured data. Experiments on both
simulated and real-world data prove the proposed algorithm to provide a
state-of-the-art performance
Task-Driven Dictionary Learning
Modeling data with linear combinations of a few elements from a learned
dictionary has been the focus of much recent research in machine learning,
neuroscience and signal processing. For signals such as natural images that
admit such sparse representations, it is now well established that these models
are well suited to restoration tasks. In this context, learning the dictionary
amounts to solving a large-scale matrix factorization problem, which can be
done efficiently with classical optimization tools. The same approach has also
been used for learning features from data for other purposes, e.g., image
classification, but tuning the dictionary in a supervised way for these tasks
has proven to be more difficult. In this paper, we present a general
formulation for supervised dictionary learning adapted to a wide variety of
tasks, and present an efficient algorithm for solving the corresponding
optimization problem. Experiments on handwritten digit classification, digital
art identification, nonlinear inverse image problems, and compressed sensing
demonstrate that our approach is effective in large-scale settings, and is well
suited to supervised and semi-supervised classification, as well as regression
tasks for data that admit sparse representations.Comment: final draft post-refereein
Digital forensic techniques for the reverse engineering of image acquisition chains
In recent years a number of new methods have been developed to detect image forgery. Most forensic techniques use footprints left on images to predict the history of the images. The images, however, sometimes could have gone through a series of processing and modification through their lifetime. It is therefore difficult to detect image tampering as the footprints could be distorted or removed over a complex chain of operations. In this research we propose digital forensic techniques that allow us to reverse engineer and determine history of images that have gone through chains of image acquisition and reproduction.
This thesis presents two different approaches to address the problem. In the first part we propose a novel theoretical framework for the reverse engineering of signal acquisition chains. Based on a simplified chain model, we describe how signals have gone in the chains at different stages using the theory of sampling signals with finite rate of innovation. Under particular conditions, our technique allows to detect whether a given signal has been reacquired through the chain. It also makes possible to predict corresponding important parameters of the chain using acquisition-reconstruction artefacts left on the signal.
The second part of the thesis presents our new algorithm for image recapture detection based on edge blurriness. Two overcomplete dictionaries are trained using the K-SVD approach to learn distinctive blurring patterns from sets of single captured and recaptured images. An SVM classifier is then built using dictionary approximation errors and the mean edge spread width from the training images. The algorithm, which requires no user intervention, was tested on a database that included more than 2500 high quality recaptured images. Our results show that our method achieves a performance rate that exceeds 99% for recaptured images and 94% for single captured images.Open Acces
Fusion-based Few-Shot Morphing Attack Detection and Fingerprinting
The vulnerability of face recognition systems to morphing attacks has posed a
serious security threat due to the wide adoption of face biometrics in the real
world. Most existing morphing attack detection (MAD) methods require a large
amount of training data and have only been tested on a few predefined attack
models. The lack of good generalization properties, especially in view of the
growing interest in developing novel morphing attacks, is a critical limitation
with existing MAD research. To address this issue, we propose to extend MAD
from supervised learning to few-shot learning and from binary detection to
multiclass fingerprinting in this paper. Our technical contributions include:
1) We propose a fusion-based few-shot learning (FSL) method to learn
discriminative features that can generalize to unseen morphing attack types
from predefined presentation attacks; 2) The proposed FSL based on the fusion
of the PRNU model and Noiseprint network is extended from binary MAD to
multiclass morphing attack fingerprinting (MAF). 3) We have collected a
large-scale database, which contains five face datasets and eight different
morphing algorithms, to benchmark the proposed few-shot MAF (FS-MAF) method.
Extensive experimental results show the outstanding performance of our
fusion-based FS-MAF. The code and data will be publicly available at
https://github.com/nz0001na/mad maf
An image recapture detection algorithm based on learning dictionaries of edge profiles
With today's digital camera technology, high-quality images can be recaptured from an liquid crystal display (LCD) monitor screen with relative ease. An attacker may choose to recapture a forged image in order to conceal imperfections and to increase its authenticity. In this paper, we address the problem of detecting images recaptured from LCD monitors. We provide a comprehensive overview of the traces found in recaptured images, and we argue that aliasing and blurriness are the least scene dependent features. We then show how aliasing can be eliminated by setting the capture parameters to predetermined values. Driven by this finding, we propose a recapture detection algorithm based on learned edge blurriness. Two sets of dictionaries are trained using the K-singular value decomposition approach from the line spread profiles of selected edges from single captured and recaptured images. An support vector machine classifier is then built using dictionary approximation errors and the mean edge spread width from the training images. The algorithm, which requires no user intervention, was tested on a database that included more than 2500 high-quality recaptured images. Our results show that our method achieves a performance rate that exceeds 99% for recaptured images and 94% for single captured images
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