42 research outputs found
Backdoor Attacks on the DNN Interpretation System
Interpretability is crucial to understand the inner workings of deep neural
networks (DNNs) and many interpretation methods generate saliency maps that
highlight parts of the input image that contribute the most to the prediction
made by the DNN. In this paper we design a backdoor attack that alters the
saliency map produced by the network for an input image only with injected
trigger that is invisible to the naked eye while maintaining the prediction
accuracy. The attack relies on injecting poisoned data with a trigger into the
training data set. The saliency maps are incorporated in the penalty term of
the objective function that is used to train a deep model and its influence on
model training is conditioned upon the presence of a trigger. We design two
types of attacks: targeted attack that enforces a specific modification of the
saliency map and untargeted attack when the importance scores of the top pixels
from the original saliency map are significantly reduced. We perform empirical
evaluation of the proposed backdoor attacks on gradient-based and gradient-free
interpretation methods for a variety of deep learning architectures. We show
that our attacks constitute a serious security threat when deploying deep
learning models developed by untrusty sources. Finally, in the Supplement we
demonstrate that the proposed methodology can be used in an inverted setting,
where the correct saliency map can be obtained only in the presence of a
trigger (key), effectively making the interpretation system available only to
selected users
Semistochastic Quadratic Bound Methods
Partition functions arise in a variety of settings, including conditional
random fields, logistic regression, and latent gaussian models. In this paper,
we consider semistochastic quadratic bound (SQB) methods for maximum likelihood
inference based on partition function optimization. Batch methods based on the
quadratic bound were recently proposed for this class of problems, and
performed favorably in comparison to state-of-the-art techniques.
Semistochastic methods fall in between batch algorithms, which use all the
data, and stochastic gradient type methods, which use small random selections
at each iteration. We build semistochastic quadratic bound-based methods, and
prove both global convergence (to a stationary point) under very weak
assumptions, and linear convergence rate under stronger assumptions on the
objective. To make the proposed methods faster and more stable, we consider
inexact subproblem minimization and batch-size selection schemes. The efficacy
of SQB methods is demonstrated via comparison with several state-of-the-art
techniques on commonly used datasets.Comment: 11 pages, 1 figur
Multi-modal Experts Network for Autonomous Driving
End-to-end learning from sensory data has shown promising results in
autonomous driving. While employing many sensors enhances world perception and
should lead to more robust and reliable behavior of autonomous vehicles, it is
challenging to train and deploy such network and at least two problems are
encountered in the considered setting. The first one is the increase of
computational complexity with the number of sensing devices. The other is the
phenomena of network overfitting to the simplest and most informative input. We
address both challenges with a novel, carefully tailored multi-modal experts
network architecture and propose a multi-stage training procedure. The network
contains a gating mechanism, which selects the most relevant input at each
inference time step using a mixed discrete-continuous policy. We demonstrate
the plausibility of the proposed approach on our 1/6 scale truck equipped with
three cameras and one LiDAR.Comment: Published at the International Conference on Robotics and Automation
(ICRA), 202