3 research outputs found
CFDP: Common Frequency Domain Pruning
As the saying goes, sometimes less is more -- and when it comes to neural
networks, that couldn't be more true. Enter pruning, the art of selectively
trimming away unnecessary parts of a network to create a more streamlined,
efficient architecture. In this paper, we introduce a novel end-to-end pipeline
for model pruning via the frequency domain. This work aims to shed light on the
interoperability of intermediate model outputs and their significance beyond
the spatial domain. Our method, dubbed Common Frequency Domain Pruning (CFDP)
aims to extrapolate common frequency characteristics defined over the feature
maps to rank the individual channels of a layer based on their level of
importance in learning the representation. By harnessing the power of CFDP, we
have achieved state-of-the-art results on CIFAR-10 with GoogLeNet reaching an
accuracy of 95.25%, that is, +0.2% from the original model. We also outperform
all benchmarks and match the original model's performance on ImageNet, using
only 55% of the trainable parameters and 60% of the FLOPs. In addition to
notable performances, models produced via CFDP exhibit robustness to a variety
of configurations including pruning from untrained neural architectures, and
resistance to adversarial attacks. The implementation code can be found at
https://github.com/Skhaki18/CFDP.Comment: CVPR ECV 2023 Accepted Pape
Forensic Considerations for the High Efficiency Image File Format (HEIF)
The High Efficiency File Format (HEIF) was adopted by Apple in 2017 as their
favoured means of capturing images from their camera application, with Android
devices such as the Galaxy S10 providing support more recently. The format is
positioned to replace JPEG as the de facto image compression file type, touting
many modern features and better compression ratios over the aging standard.
However, while millions of devices across the world are already able to produce
HEIF files, digital forensics research has not given the format much attention.
As HEIF is a complex container format, much different from traditional still
picture formats, this leaves forensics practitioners exposed to risks of
potentially mishandling evidence. This paper describes the forensically
relevant features of the HEIF format, including those which could be used to
hide data, or cause issues in an investigation, while also providing commentary
on the state of software support for the format. Finally, suggestions for
current best-practice are provided, before discussing the requirements of a
forensically robust HEIF analysis tool.Comment: 8 pages, conference paper pre-prin