4,607 research outputs found
Adversarial attacks hidden in plain sight
Convolutional neural networks have been used to achieve a string of successes
during recent years, but their lack of interpretability remains a serious
issue. Adversarial examples are designed to deliberately fool neural networks
into making any desired incorrect classification, potentially with very high
certainty. Several defensive approaches increase robustness against adversarial
attacks, demanding attacks of greater magnitude, which lead to visible
artifacts. By considering human visual perception, we compose a technique that
allows to hide such adversarial attacks in regions of high complexity, such
that they are imperceptible even to an astute observer. We carry out a user
study on classifying adversarially modified images to validate the perceptual
quality of our approach and find significant evidence for its concealment with
regards to human visual perception
MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis
Interpretability has emerged as a crucial aspect of machine learning, aimed
at providing insights into the working of complex neural networks. However,
existing solutions vary vastly based on the nature of the interpretability
task, with each use case requiring substantial time and effort. This paper
introduces MARGIN, a simple yet general approach to address a large set of
interpretability tasks ranging from identifying prototypes to explaining image
predictions. MARGIN exploits ideas rooted in graph signal analysis to determine
influential nodes in a graph, which are defined as those nodes that maximally
describe a function defined on the graph. By carefully defining task-specific
graphs and functions, we demonstrate that MARGIN outperforms existing
approaches in a number of disparate interpretability challenges.Comment: Technical Repor
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