44,167 research outputs found
EviPlant: An efficient digital forensic challenge creation, manipulation and distribution solution
Education and training in digital forensics requires a variety of suitable
challenge corpora containing realistic features including regular
wear-and-tear, background noise, and the actual digital traces to be discovered
during investigation. Typically, the creation of these challenges requires
overly arduous effort on the part of the educator to ensure their viability.
Once created, the challenge image needs to be stored and distributed to a class
for practical training. This storage and distribution step requires significant
time and resources and may not even be possible in an online/distance learning
scenario due to the data sizes involved. As part of this paper, we introduce a
more capable methodology and system as an alternative to current approaches.
EviPlant is a system designed for the efficient creation, manipulation, storage
and distribution of challenges for digital forensics education and training.
The system relies on the initial distribution of base disk images, i.e., images
containing solely base operating systems. In order to create challenges for
students, educators can boot the base system, emulate the desired activity and
perform a "diffing" of resultant image and the base image. This diffing process
extracts the modified artefacts and associated metadata and stores them in an
"evidence package". Evidence packages can be created for different personae,
different wear-and-tear, different emulated crimes, etc., and multiple evidence
packages can be distributed to students and integrated into the base images. A
number of additional applications in digital forensic challenge creation for
tool testing and validation, proficiency testing, and malware analysis are also
discussed as a result of using EviPlant.Comment: Digital Forensic Research Workshop Europe 201
SINVAD: Search-based Image Space Navigation for DNN Image Classifier Test Input Generation
The testing of Deep Neural Networks (DNNs) has become increasingly important
as DNNs are widely adopted by safety critical systems. While many test adequacy
criteria have been suggested, automated test input generation for many types of
DNNs remains a challenge because the raw input space is too large to randomly
sample or to navigate and search for plausible inputs. Consequently, current
testing techniques for DNNs depend on small local perturbations to existing
inputs, based on the metamorphic testing principle. We propose new ways to
search not over the entire image space, but rather over a plausible input space
that resembles the true training distribution. This space is constructed using
Variational Autoencoders (VAEs), and navigated through their latent vector
space. We show that this space helps efficiently produce test inputs that can
reveal information about the robustness of DNNs when dealing with realistic
tests, opening the field to meaningful exploration through the space of highly
structured images
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
We present DeblurGAN, an end-to-end learned method for motion deblurring. The
learning is based on a conditional GAN and the content loss . DeblurGAN
achieves state-of-the art performance both in the structural similarity measure
and visual appearance. The quality of the deblurring model is also evaluated in
a novel way on a real-world problem -- object detection on (de-)blurred images.
The method is 5 times faster than the closest competitor -- DeepDeblur. We also
introduce a novel method for generating synthetic motion blurred images from
sharp ones, allowing realistic dataset augmentation.
The model, code and the dataset are available at
https://github.com/KupynOrest/DeblurGANComment: CVPR 2018 camera-read
Wind energy system time-domain (WEST) analyzers
A portable analyzer which simulates in real time the complex nonlinear dynamics of horizontal axis wind energy systems was constructed. Math models for an aeroelastic rotor featuring nonlinear aerodynamic and inertial terms were implemented with high speed digital controllers and analog calculation. This model was combined with other math models of elastic supports, control systems, a power train and gimballed rotor kinematics. A stroboscopic display system graphically depicting distributed blade loads, motion, and other aerodynamic functions on a cathode ray tube is included. Limited correlation efforts showed good comparison between the results of this analyzer and other sophisticated digital simulations. The digital simulation results were successfully correlated with test data
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