How to Find More Supernovae with Less Work: Object Classification Techniques for Difference Imaging

We present the results of applying new object classification techniques to difference images in the context of the Nearby Supernova Factory supernova search. Most current supernova searches subtract reference images from new images, identify objects in these difference images, and apply simple threshold cuts on parameters such as statistical significance, shape, and motion to reject objects such as cosmic rays, asteroids, and subtraction artifacts. Although most static objects subtract cleanly, even a very low false positive detection rate can lead to hundreds of non-supernova candidates which must be vetted by human inspection before triggering additional followup. In comparison to simple threshold cuts, more sophisticated methods such as Boosted Decision Trees, Random Forests, and Support Vector Machines provide dramatically better object discrimination. At the Nearby Supernova Factory, we reduced the number of non-supernova candidates by a factor of 10 while increasing our supernova identification efficiency. Methods such as these will be crucial for maintaining a reasonable false positive rate in the automated transient alert pipelines of upcoming projects such as PanSTARRS and LSST.Comment: 25 pages; 6 figures; submitted to Ap

From Data Topology to a Modular Classifier

This article describes an approach to designing a distributed and modular neural classifier. This approach introduces a new hierarchical clustering that enables one to determine reliable regions in the representation space by exploiting supervised information. A multilayer perceptron is then associated with each of these detected clusters and charged with recognizing elements of the associated cluster while rejecting all others. The obtained global classifier is comprised of a set of cooperating neural networks and completed by a K-nearest neighbor classifier charged with treating elements rejected by all the neural networks. Experimental results for the handwritten digit recognition problem and comparison with neural and statistical nonmodular classifiers are given

Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication

We investigate whether a classifier can continuously authenticate users based on the way they interact with the touchscreen of a smart phone. We propose a set of 30 behavioral touch features that can be extracted from raw touchscreen logs and demonstrate that different users populate distinct subspaces of this feature space. In a systematic experiment designed to test how this behavioral pattern exhibits consistency over time, we collected touch data from users interacting with a smart phone using basic navigation maneuvers, i.e., up-down and left-right scrolling. We propose a classification framework that learns the touch behavior of a user during an enrollment phase and is able to accept or reject the current user by monitoring interaction with the touch screen. The classifier achieves a median equal error rate of 0% for intra-session authentication, 2%-3% for inter-session authentication and below 4% when the authentication test was carried out one week after the enrollment phase. While our experimental findings disqualify this method as a standalone authentication mechanism for long-term authentication, it could be implemented as a means to extend screen-lock time or as a part of a multi-modal biometric authentication system.Comment: to appear at IEEE Transactions on Information Forensics & Security; Download data from http://www.mariofrank.net/touchalytics

Evaluating Adversarial Robustness of Detection-based Defenses against Adversarial Examples

Machine Learning algorithms provide astonishing performance in a wide range of tasks, including sensitive and critical applications. On the other hand, it has been shown that they are vulnerable to adversarial attacks, a set of techniques that violate the integrity, confidentiality, or availability of such systems. In particular, one of the most studied phenomena concerns adversarial examples, i.e., input samples that are carefully manipulated to alter the model output. In the last decade, the research community put a strong effort into this field, proposing new evasion attacks and methods to defend against them. With this thesis, we propose different approaches that can be applied to Deep Neural Networks to detect and reject adversarial examples that present an anomalous distribution with respect to training data. The first leverages the domain knowledge of the relationships among the considered classes integrated through a framework in which first-order logic knowledge is converted into constraints and injected into a semi-supervised learning problem. Within this setting, the classifier is able to reject samples that violate the domain knowledge constraints. This approach can be applied in both single and multi-label classification settings. The second one is based on a Deep Neural Rejection (DNR) mechanism to detect adversarial examples, based on the idea of rejecting samples that exhibit anomalous feature representations at different network layers. To this end, we exploit RBF SVM classifiers, which provide decreasing confidence values as samples move away from the training data distribution. Despite technical differences, this approach shares a common backbone structure with other proposed methods that we formalize in a unifying framework. As all of them require comparing input samples against an oversized number of reference prototypes, possibly at different representation layers, they suffer from the same drawback, i.e., high computational overhead and memory usage, that makes these approaches unusable in real applications. To overcome this limitation, we introduce FADER (Fast Adversarial Example Rejection), a technique for speeding up detection-based methods by employing RBF networks as detectors: by fixing the number of required prototypes, their runtime complexity can be controlled. All proposed methods are evaluated in both black-box and white-box settings, i.e., against an attacker unaware of the defense mechanism, and against an attacker who knows the defense and adapts the attack algorithm to bypass it, respectively. Our experimental evaluation shows that the proposed methods increase the robustness of the defended models and help detect adversarial examples effectively, especially when the attacker does not know the underlying detection system

Towards Personalized Medicine Using Systems Biology And Machine Learning

The rate of acquiring biological data has greatly surpassed our ability to interpret it. At the same time, we have started to understand that evolution of many diseases such as cancer, are the results of the interplay between the disease itself and the immune system of the host. It is now well accepted that cancer is not a single disease, but a “complex collection of distinct genetic diseases united by common hallmarks”. Understanding the differences between such disease subtypes is key not only in providing adequate treatments for known subtypes but also identifying new ones. These unforeseen disease subtypes are one of the main reasons high-profile clinical trials fail. To identify such cases, we proposed a classification technique, based on Support Vector Machines, that is able to automatically identify samples that are dissimilar from the classes used for training. We assessed the performance of this approach both with artificial data and data from the UCI machine learning repository. Moreover, we showed in a leukemia experiment that our method is able to identify 65% of the MLL patients when it was trained only on AML vs. ALL. In addition, to augment our ability to understand the disease mechanism in each subgroup, we proposed a systems biology approach able to consider all measured gene expressing changes, thus eliminating the possibility that small but important gene changes (e.g. transcription factors) are omitted from the analysis. We showed that this approach provides consistent results that do not depend on the choice of an arbitrary threshold for the differential regulation. We also showed in a multiple sclerosis study that this approach is able to obtain consistent results across multiple experiments performed by different groups on different technologies, that could not be achieved based solely using differential expression. The cut-off free impact analysis was released as part of the ROntoTools Bioconductor package