376 research outputs found
Efficient Defenses Against Adversarial Attacks
Following the recent adoption of deep neural networks (DNN) accross a wide
range of applications, adversarial attacks against these models have proven to
be an indisputable threat. Adversarial samples are crafted with a deliberate
intention of undermining a system. In the case of DNNs, the lack of better
understanding of their working has prevented the development of efficient
defenses. In this paper, we propose a new defense method based on practical
observations which is easy to integrate into models and performs better than
state-of-the-art defenses. Our proposed solution is meant to reinforce the
structure of a DNN, making its prediction more stable and less likely to be
fooled by adversarial samples. We conduct an extensive experimental study
proving the efficiency of our method against multiple attacks, comparing it to
numerous defenses, both in white-box and black-box setups. Additionally, the
implementation of our method brings almost no overhead to the training
procedure, while maintaining the prediction performance of the original model
on clean samples.Comment: 16 page
FastSal: a Computationally Efficient Network for Visual Saliency Prediction
This paper focuses on the problem of visual saliency prediction, predicting
regions of an image that tend to attract human visual attention, under a
constrained computational budget. We modify and test various recent efficient
convolutional neural network architectures like EfficientNet and MobileNetV2
and compare them with existing state-of-the-art saliency models such as SalGAN
and DeepGaze II both in terms of standard accuracy metrics like AUC and NSS,
and in terms of the computational complexity and model size. We find that
MobileNetV2 makes an excellent backbone for a visual saliency model and can be
effective even without a complex decoder. We also show that knowledge transfer
from a more computationally expensive model like DeepGaze II can be achieved
via pseudo-labelling an unlabelled dataset, and that this approach gives result
on-par with many state-of-the-art algorithms with a fraction of the
computational cost and model size. Source code is available at
https://github.com/feiyanhu/FastSal
Road Redesign Technique Achieving Enhanced Road Safety by Inpainting with a Diffusion Model
Road infrastructure can affect the occurrence of road accidents. Therefore,
identifying roadway features with high accident probability is crucial. Here,
we introduce image inpainting that can assist authorities in achieving safe
roadway design with minimal intervention in the current roadway structure.
Image inpainting is based on inpainting safe roadway elements in a roadway
image, replacing accident-prone (AP) features by using a diffusion model. After
object-level segmentation, the AP features identified by the properties of
accident hotspots are masked by a human operator and safe roadway elements are
inpainted. With only an average time of 2 min for image inpainting, the
likelihood of an image being classified as an accident hotspot drops by an
average of 11.85%. In addition, safe urban spaces can be designed considering
human factors of commuters such as gaze saliency. Considering this, we
introduce saliency enhancement that suggests chrominance alteration for a safe
road view.Comment: 9 Pages, 6 figures, 4 table
Unobtrusive and pervasive video-based eye-gaze tracking
Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe
Local Mismatch Location and Spatial Scale Detection in Image Registration
Image registration is now a well understood problem and several techniques using a combination of cost functions,
transformation models and optimizers have been reported in medical imaging literature. Parametric methods
often rely on the efficient placement of control points in the images, that is, depending on the location and scale
at which images are mismatched. Poor choice of parameterization results in deformations not being modeled
accurately or over parameterization, where control points may lie in homogeneous regions with low sensitivity to
cost. This lowers computational efficiency due to the high complexity of the search space and might also provide
transformations that are not physically meaningful, and possibly folded.
Adaptive methods that parameterize based on mismatch in images have been proposed. In such methods, the
cost measure must be normalized, heuristics such as how many points to pick, resolution of the grids, choosing
gradient thresholds and when to refine scale would have to be ascertained in addition to the limitation of working
only at a few discrete scales.
In this paper we identify mismatch by searching the entire image and a wide range of smooth spatial scales.
The mismatch vector, containing location and scale of mismatch is computed from peaks in the local joint
entropy. Results show that this method can be used to quickly and effectively locate mismatched regions in
images where control points can be placed in preference to other regions speeding up registration.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85931/1/Fessler223.pd
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