38,824 research outputs found
Understanding the Impact of Adversarial Robustness on Accuracy Disparity
While it has long been empirically observed that adversarial robustness may
be at odds with standard accuracy and may have further disparate impacts on
different classes, it remains an open question to what extent such observations
hold and how the class imbalance plays a role within. In this paper, we attempt
to understand this question of accuracy disparity by taking a closer look at
linear classifiers under a Gaussian mixture model. We decompose the impact of
adversarial robustness into two parts: an inherent effect that will degrade the
standard accuracy on all classes due to the robustness constraint, and the
other caused by the class imbalance ratio, which will increase the accuracy
disparity compared to standard training. Furthermore, we also show that such
effects extend beyond the Gaussian mixture model, by generalizing our data
model to the general family of stable distributions. More specifically, we
demonstrate that while the constraint of adversarial robustness consistently
degrades the standard accuracy in the balanced class setting, the class
imbalance ratio plays a fundamentally different role in accuracy disparity
compared to the Gaussian case, due to the heavy tail of the stable
distribution. We additionally perform experiments on both synthetic and
real-world datasets to corroborate our theoretical findings. Our empirical
results also suggest that the implications may extend to nonlinear models over
real-world datasets. Our code is publicly available on GitHub at
https://github.com/Accuracy-Disparity/AT-on-AD.Comment: Accepted at ICML 202
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
We investigate conditions under which test statistics exist that can reliably
detect examples, which have been adversarially manipulated in a white-box
attack. These statistics can be easily computed and calibrated by randomly
corrupting inputs. They exploit certain anomalies that adversarial attacks
introduce, in particular if they follow the paradigm of choosing perturbations
optimally under p-norm constraints. Access to the log-odds is the only
requirement to defend models. We justify our approach empirically, but also
provide conditions under which detectability via the suggested test statistics
is guaranteed to be effective. In our experiments, we show that it is even
possible to correct test time predictions for adversarial attacks with high
accuracy
DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments
Simultaneous Localization and Mapping (SLAM) is considered to be a
fundamental capability for intelligent mobile robots. Over the past decades,
many impressed SLAM systems have been developed and achieved good performance
under certain circumstances. However, some problems are still not well solved,
for example, how to tackle the moving objects in the dynamic environments, how
to make the robots truly understand the surroundings and accomplish advanced
tasks. In this paper, a robust semantic visual SLAM towards dynamic
environments named DS-SLAM is proposed. Five threads run in parallel in
DS-SLAM: tracking, semantic segmentation, local mapping, loop closing, and
dense semantic map creation. DS-SLAM combines semantic segmentation network
with moving consistency check method to reduce the impact of dynamic objects,
and thus the localization accuracy is highly improved in dynamic environments.
Meanwhile, a dense semantic octo-tree map is produced, which could be employed
for high-level tasks. We conduct experiments both on TUM RGB-D dataset and in
the real-world environment. The results demonstrate the absolute trajectory
accuracy in DS-SLAM can be improved by one order of magnitude compared with
ORB-SLAM2. It is one of the state-of-the-art SLAM systems in high-dynamic
environments. Now the code is available at our github:
https://github.com/ivipsourcecode/DS-SLAMComment: 7 pages, accepted at the 2018 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2018). Now the code is available at our
github: https://github.com/ivipsourcecode/DS-SLA
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