1,970 research outputs found
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid
Deep neural networks have been widely adopted in recent years, exhibiting
impressive performances in several application domains. It has however been
shown that they can be fooled by adversarial examples, i.e., images altered by
a barely-perceivable adversarial noise, carefully crafted to mislead
classification. In this work, we aim to evaluate the extent to which
robot-vision systems embodying deep-learning algorithms are vulnerable to
adversarial examples, and propose a computationally efficient countermeasure to
mitigate this threat, based on rejecting classification of anomalous inputs. We
then provide a clearer understanding of the safety properties of deep networks
through an intuitive empirical analysis, showing that the mapping learned by
such networks essentially violates the smoothness assumption of learning
algorithms. We finally discuss the main limitations of this work, including the
creation of real-world adversarial examples, and sketch promising research
directions.Comment: Accepted for publication at the ICCV 2017 Workshop on Vision in
Practice on Autonomous Robots (ViPAR
Multiclass Learning Approaches: A Theoretical Comparison with Implications
We theoretically analyze and compare the following five popular multiclass
classification methods: One vs. All, All Pairs, Tree-based classifiers, Error
Correcting Output Codes (ECOC) with randomly generated code matrices, and
Multiclass SVM. In the first four methods, the classification is based on a
reduction to binary classification. We consider the case where the binary
classifier comes from a class of VC dimension , and in particular from the
class of halfspaces over . We analyze both the estimation error and
the approximation error of these methods. Our analysis reveals interesting
conclusions of practical relevance, regarding the success of the different
approaches under various conditions. Our proof technique employs tools from VC
theory to analyze the \emph{approximation error} of hypothesis classes. This is
in sharp contrast to most, if not all, previous uses of VC theory, which only
deal with estimation error
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