30,437 research outputs found
A Generative Framework for Low-Cost Result Validation of Outsourced Machine Learning Tasks
The growing popularity of Machine Learning (ML) has led to its deployment in
various sensitive domains, which has resulted in significant research focused
on ML security and privacy. However, in some applications, such as autonomous
driving, integrity verification of the outsourced ML workload is more
critical--a facet that has not received much attention. Existing solutions,
such as multi-party computation and proof-based systems, impose significant
computation overhead, which makes them unfit for real-time applications. We
propose Fides, a novel framework for real-time validation of outsourced ML
workloads. Fides features a novel and efficient distillation technique--Greedy
Distillation Transfer Learning--that dynamically distills and fine-tunes a
space and compute-efficient verification model for verifying the corresponding
service model while running inside a trusted execution environment. Fides
features a client-side attack detection model that uses statistical analysis
and divergence measurements to identify, with a high likelihood, if the service
model is under attack. Fides also offers a re-classification functionality that
predicts the original class whenever an attack is identified. We devised a
generative adversarial network framework for training the attack detection and
re-classification models. The evaluation shows that Fides achieves an accuracy
of up to 98% for attack detection and 94% for re-classification.Comment: 16 pages, 11 figure
Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks
Recognizing Traffic Signs using intelligent systems can drastically reduce
the number of accidents happening world-wide. With the arrival of Self-driving
cars it has become a staple challenge to solve the automatic recognition of
Traffic and Hand-held signs in the major streets. Various machine learning
techniques like Random Forest, SVM as well as deep learning models has been
proposed for classifying traffic signs. Though they reach state-of-the-art
performance on a particular data-set, but fall short of tackling multiple
Traffic Sign Recognition benchmarks. In this paper, we propose a novel and
one-for-all architecture that aces multiple benchmarks with better overall
score than the state-of-the-art architectures. Our model is made of residual
convolutional blocks with hierarchical dilated skip connections joined in
steps. With this we score 99.33% Accuracy in German sign recognition benchmark
and 99.17% Accuracy in Belgian traffic sign classification benchmark. Moreover,
we propose a newly devised dilated residual learning representation technique
which is very low in both memory and computational complexity
Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification
Safety is an essential aspect in the facilitation of automated vehicle
deployment. Current testing practices are not enough, and going beyond them
leads to infeasible testing requirements, such as needing to drive billions of
kilometres on public roads. Automated vehicles are exposed to an indefinite
number of scenarios. Handling of the most challenging scenarios should be
tested, which leads to the question of how such corner cases can be determined.
We propose an approach to identify the performance boundary, where these corner
cases are located, using Gaussian Process Classification. We also demonstrate
the classification on an exemplary traffic jam approach scenario, showing that
it is feasible and would lead to more efficient testing practices.Comment: 6 pages, 5 figures, accepted at 2019 IEEE Intelligent Transportation
Systems Conference - ITSC 2019, Auckland, New Zealand, October 201
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