Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: multi-patch aggregation models for resampling detection   Images captured nowadays are of varying dimensions with smartphones and
DSLR's allowing users to choose from a list of available image resolutions. It
is therefore imperative for forensic algorithms such as resampling detection to
scale well for images of varying dimensions. However, in our experiments, we
observed that many state-of-the-art forensic algorithms are sensitive to image
size and their performance quickly degenerates when operated on images of
diverse dimensions despite re-training them using multiple image sizes. To
handle this issue, we propose a novel pooling strategy called ITERATIVE
POOLING. This pooling strategy can dynamically adjust input tensors in a
discrete without much loss of information as in ROI Max-pooling. This pooling
strategy can be used with any of the existing deep models and for demonstration
purposes, we show its utility on Resnet-18 for the case of resampling detection
a fundamental operation for any image sought of image manipulation. Compared to
existing strategies and Max-pooling it gives up to 7-8% improvement on public
datasets.

27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph