7,052 research outputs found
On the Subspace of Image Gradient Orientations
We introduce the notion of Principal Component Analysis (PCA) of image
gradient orientations. As image data is typically noisy, but noise is
substantially different from Gaussian, traditional PCA of pixel intensities
very often fails to estimate reliably the low-dimensional subspace of a given
data population. We show that replacing intensities with gradient orientations
and the norm with a cosine-based distance measure offers, to some
extend, a remedy to this problem. Our scheme requires the eigen-decomposition
of a covariance matrix and is as computationally efficient as standard
PCA. We demonstrate some of its favorable properties on robust subspace
estimation
QEBA: Query-Efficient Boundary-Based Blackbox Attack
Machine learning (ML), especially deep neural networks (DNNs) have been
widely used in various applications, including several safety-critical ones
(e.g. autonomous driving). As a result, recent research about adversarial
examples has raised great concerns. Such adversarial attacks can be achieved by
adding a small magnitude of perturbation to the input to mislead model
prediction. While several whitebox attacks have demonstrated their
effectiveness, which assume that the attackers have full access to the machine
learning models; blackbox attacks are more realistic in practice. In this
paper, we propose a Query-Efficient Boundary-based blackbox Attack (QEBA) based
only on model's final prediction labels. We theoretically show why previous
boundary-based attack with gradient estimation on the whole gradient space is
not efficient in terms of query numbers, and provide optimality analysis for
our dimension reduction-based gradient estimation. On the other hand, we
conducted extensive experiments on ImageNet and CelebA datasets to evaluate
QEBA. We show that compared with the state-of-the-art blackbox attacks, QEBA is
able to use a smaller number of queries to achieve a lower magnitude of
perturbation with 100% attack success rate. We also show case studies of
attacks on real-world APIs including MEGVII Face++ and Microsoft Azure.Comment: Accepted by CVPR 202
Face recognition in different subspaces - A comparative study
Face recognition is one of the most successful applications of image analysis and understanding and has gained much attention in recent years. Among many approaches to the problem of face recognition, appearance-based subspace analysis still gives the most promising results. In this paper we study the three most popular appearance-based face recognition projection methods (PCA, LDA and ICA). All methods are tested in equal working conditions regarding preprocessing and algorithm implementation on the FERET data set with its standard tests. We also compare the ICA method with its whitening preprocess and find out that there is no significant difference between them. When we compare different projection with different metrics we found out that the LDA+COS combination is the most promising for all tasks. The L1 metric gives the best results in
combination with PCA and ICA1, and COS is superior to any other metric when used with LDA and ICA2. Our results are compared to other studies and some discrepancies are pointed ou
Shape and Texture Combined Face Recognition for Detection of Forged ID Documents
This paper proposes a face recognition system that can be used to effectively match a face image scanned from an identity (ID) doc-ument against the face image stored in the biometric chip of such a document. The purpose of this specific face recognition algorithm is to aid the automatic detection of forged ID documents where the photography printed on the document’s surface has been altered or replaced. The proposed algorithm uses a novel combination of texture and shape features together with sub-space representation techniques. In addition, the robustness of the proposed algorithm when dealing with more general face recognition tasks has been proven with the Good, the Bad & the Ugly (GBU) dataset, one of the most challenging datasets containing frontal faces. The proposed algorithm has been complement-ed with a novel method that adopts two operating points to enhance the reliability of the algorithm’s final verification decision.Final Accepted Versio
Randomized Robust Subspace Recovery for High Dimensional Data Matrices
This paper explores and analyzes two randomized designs for robust Principal
Component Analysis (PCA) employing low-dimensional data sketching. In one
design, a data sketch is constructed using random column sampling followed by
low dimensional embedding, while in the other, sketching is based on random
column and row sampling. Both designs are shown to bring about substantial
savings in complexity and memory requirements for robust subspace learning over
conventional approaches that use the full scale data. A characterization of the
sample and computational complexity of both designs is derived in the context
of two distinct outlier models, namely, sparse and independent outlier models.
The proposed randomized approach can provably recover the correct subspace with
computational and sample complexity that are almost independent of the size of
the data. The results of the mathematical analysis are confirmed through
numerical simulations using both synthetic and real data
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