107,343 research outputs found
Boosting Image Forgery Detection using Resampling Features and Copy-move analysis
Realistic image forgeries involve a combination of splicing, resampling,
cloning, region removal and other methods. While resampling detection
algorithms are effective in detecting splicing and resampling, copy-move
detection algorithms excel in detecting cloning and region removal. In this
paper, we combine these complementary approaches in a way that boosts the
overall accuracy of image manipulation detection. We use the copy-move
detection method as a pre-filtering step and pass those images that are
classified as untampered to a deep learning based resampling detection
framework. Experimental results on various datasets including the 2017 NIST
Nimble Challenge Evaluation dataset comprising nearly 10,000 pristine and
tampered images shows that there is a consistent increase of 8%-10% in
detection rates, when copy-move algorithm is combined with different resampling
detection algorithms
Negative association, ordering and convergence of resampling methods
We study convergence and convergence rates for resampling schemes. Our first
main result is a general consistency theorem based on the notion of negative
association, which is applied to establish the almost-sure weak convergence of
measures output from Kitagawa's (1996) stratified resampling method. Carpenter
et al's (1999) systematic resampling method is similar in structure but can
fail to converge depending on the order of the input samples. We introduce a
new resampling algorithm based on a stochastic rounding technique of Srinivasan
(2001), which shares some attractive properties of systematic resampling, but
which exhibits negative association and therefore converges irrespective of the
order of the input samples. We confirm a conjecture made by Kitagawa (1996)
that ordering input samples by their states in yields a faster
rate of convergence; we establish that when particles are ordered using the
Hilbert curve in , the variance of the resampling error is
under mild conditions, where
is the number of particles. We use these results to establish asymptotic
properties of particle algorithms based on resampling schemes that differ from
multinomial resampling.Comment: 54 pages, including 30 pages of supplementary materials (a typo in
Algorithm 1 has been corrected
A Bootstrap Procedure for Panel Datasets with Many Cross-Sectional Units
This paper considers the issue of bootstrap resampling in panel datasets. The availability of datasets with large temporal and cross sectional dimensions suggests the possibility of new resampling schemes. We suggest one possibility which has not been widely explored in the literature. It amounts to constructing bootstrap samples by resampling whole cross sectional units with replacement. In cases where the data do not exhibit cross sectional dependence but exhibit temporal dependence, such a resampling scheme is of great interest as it allows the application of i.i.d. bootstrap resampling rather than block bootstrap resampling. It is well known that the former enables superior approximation to distributions of statistics compared to the latter. We prove that the bootstrap based on cross sectional resampling provides asymptotic refinements. A Monte Carlo study illustrates the superior properties of the new resampling scheme compared to the block bootstrap.Bootstrap, Panel data
Comparison of Resampling Schemes for Particle Filtering
This contribution is devoted to the comparison of various resampling
approaches that have been proposed in the literature on particle filtering. It
is first shown using simple arguments that the so-called residual and
stratified methods do yield an improvement over the basic multinomial
resampling approach. A simple counter-example showing that this property does
not hold true for systematic resampling is given. Finally, some results on the
large-sample behavior of the simple bootstrap filter algorithm are given. In
particular, a central limit theorem is established for the case where
resampling is performed using the residual approach
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