43,371 research outputs found
Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers
Machine Learning (ML) algorithms are used to train computers to perform a
variety of complex tasks and improve with experience. Computers learn how to
recognize patterns, make unintended decisions, or react to a dynamic
environment. Certain trained machines may be more effective than others because
they are based on more suitable ML algorithms or because they were trained
through superior training sets. Although ML algorithms are known and publicly
released, training sets may not be reasonably ascertainable and, indeed, may be
guarded as trade secrets. While much research has been performed about the
privacy of the elements of training sets, in this paper we focus our attention
on ML classifiers and on the statistical information that can be unconsciously
or maliciously revealed from them. We show that it is possible to infer
unexpected but useful information from ML classifiers. In particular, we build
a novel meta-classifier and train it to hack other classifiers, obtaining
meaningful information about their training sets. This kind of information
leakage can be exploited, for example, by a vendor to build more effective
classifiers or to simply acquire trade secrets from a competitor's apparatus,
potentially violating its intellectual property rights
Pre and Post-hoc Diagnosis and Interpretation of Malignancy from Breast DCE-MRI
We propose a new method for breast cancer screening from DCE-MRI based on a
post-hoc approach that is trained using weakly annotated data (i.e., labels are
available only at the image level without any lesion delineation). Our proposed
post-hoc method automatically diagnosis the whole volume and, for positive
cases, it localizes the malignant lesions that led to such diagnosis.
Conversely, traditional approaches follow a pre-hoc approach that initially
localises suspicious areas that are subsequently classified to establish the
breast malignancy -- this approach is trained using strongly annotated data
(i.e., it needs a delineation and classification of all lesions in an image).
Another goal of this paper is to establish the advantages and disadvantages of
both approaches when applied to breast screening from DCE-MRI. Relying on
experiments on a breast DCE-MRI dataset that contains scans of 117 patients,
our results show that the post-hoc method is more accurate for diagnosing the
whole volume per patient, achieving an AUC of 0.91, while the pre-hoc method
achieves an AUC of 0.81. However, the performance for localising the malignant
lesions remains challenging for the post-hoc method due to the weakly labelled
dataset employed during training.Comment: Submitted to Medical Image Analysi
Using Sensor Metadata Streams to Identify Topics of Local Events in the City
In this paper, we study the emerging Information Retrieval (IR) task of local event retrieval using sensor metadata streams. Sensor metadata streams include information such as the crowd density from video processing, audio classifications, and social media activity. We propose to use these metadata streams to identify the topics of local events within a city, where each event topic corresponds to a set of terms representing a type of events such as a concert or a protest. We develop a supervised approach that is capable of mapping sensor metadata observations to an event topic. In addition to using a variety of sensor metadata observations about the current status of the environment as learning features, our approach incorporates additional background features to model cyclic event patterns. Through experimentation with data collected from two locations in a major Spanish city, we show that our approach markedly outperforms an alternative baseline. We also show that modelling background information improves event topic identification
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