6,716 research outputs found
Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems
Voice Processing Systems (VPSes), now widely deployed, have been made
significantly more accurate through the application of recent advances in
machine learning. However, adversarial machine learning has similarly advanced
and has been used to demonstrate that VPSes are vulnerable to the injection of
hidden commands - audio obscured by noise that is correctly recognized by a VPS
but not by human beings. Such attacks, though, are often highly dependent on
white-box knowledge of a specific machine learning model and limited to
specific microphones and speakers, making their use across different acoustic
hardware platforms (and thus their practicality) limited. In this paper, we
break these dependencies and make hidden command attacks more practical through
model-agnostic (blackbox) attacks, which exploit knowledge of the signal
processing algorithms commonly used by VPSes to generate the data fed into
machine learning systems. Specifically, we exploit the fact that multiple
source audio samples have similar feature vectors when transformed by acoustic
feature extraction algorithms (e.g., FFTs). We develop four classes of
perturbations that create unintelligible audio and test them against 12 machine
learning models, including 7 proprietary models (e.g., Google Speech API, Bing
Speech API, IBM Speech API, Azure Speaker API, etc), and demonstrate successful
attacks against all targets. Moreover, we successfully use our maliciously
generated audio samples in multiple hardware configurations, demonstrating
effectiveness across both models and real systems. In so doing, we demonstrate
that domain-specific knowledge of audio signal processing represents a
practical means of generating successful hidden voice command attacks
A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation
Traditionally, abnormal heart sound classification is framed as a three-stage
process. The first stage involves segmenting the phonocardiogram to detect
fundamental heart sounds; after which features are extracted and classification
is performed. Some researchers in the field argue the segmentation step is an
unwanted computational burden, whereas others embrace it as a prior step to
feature extraction. When comparing accuracies achieved by studies that have
segmented heart sounds before analysis with those who have overlooked that
step, the question of whether to segment heart sounds before feature extraction
is still open. In this study, we explicitly examine the importance of heart
sound segmentation as a prior step for heart sound classification, and then
seek to apply the obtained insights to propose a robust classifier for abnormal
heart sound detection. Furthermore, recognizing the pressing need for
explainable Artificial Intelligence (AI) models in the medical domain, we also
unveil hidden representations learned by the classifier using model
interpretation techniques. Experimental results demonstrate that the
segmentation plays an essential role in abnormal heart sound classification.
Our new classifier is also shown to be robust, stable and most importantly,
explainable, with an accuracy of almost 100% on the widely used PhysioNet
dataset
Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop
The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques
specifically developed for analyzing and understanding the inner-workings and
representations acquired by neural models of language. Approaches included:
systematic manipulation of input to neural networks and investigating the
impact on their performance, testing whether interpretable knowledge can be
decoded from intermediate representations acquired by neural networks,
proposing modifications to neural network architectures to make their knowledge
state or generated output more explainable, and examining the performance of
networks on simplified or formal languages. Here we review a number of
representative studies in each category
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