Implementing the Projected Spatial Rich Features on a GPU
Oxford University, Department of Computer Science, Parks Road, Oxford OX1 3QD, England
Oxford University, 2014
@article{ker2014implementing,
title={Implementing the Projected Spatial Rich Features on a GPU},
author={Ker, Andrew D.},
year={2014}
}
The Projected Spatial Rich Model (PSRM) generates powerful steganalysis features, but requires the calculation of tens of thousands of convolutions with image noise residuals. This makes it very slow: the reference implementation takes an impractical 20{30 minutes per 1 megapixel (Mpix) image. We present a case study which first tweaks the definition of the PSRM features, to make them more efficient, and then optimizes an implementation on GPU hardware which exploits their parallelism (whilst avoiding the worst of their sequentiality). Some nonstandard CUDA techniques are used. Even with only a single GPU, the time for feature calculation is reduced by three orders of magnitude, and the detection power is reduced only marginally.
February 11, 2014 by hgpu
