• About
    • Login
    View Item 
    •   Institutional Repository Home
    • Electronic Theses and Dissertations
    • Electronic Theses and Dissertations
    • View Item
    •   Institutional Repository Home
    • Electronic Theses and Dissertations
    • Electronic Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Institutional RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDepartmentThis CollectionBy Issue DateAuthorsTitlesSubjectsDepartment

    My Account

    LoginRegister

    Combining Reachable Set Computation with Neuron Coverage

    Yu, Ulysses
    0000-0003-3587-2557
    : http://hdl.handle.net/1803/15959
    : 2020-07-24

    Abstract

    As the use of Deep Neural Networks (DNN) continues to increase in the field of Cyber Physical Systems (CPS), we need more methods of guaranteeing their safety. Two recent areas of research into safety of DNNs have been in reachability analysis and neuron coverage. Reachability analysis takes a high dimensional input set, such as hyperrectangles or polytopes, and propagates it through the network to create an output set, and certain properties are tested on the output set to verify the safety of the network. Neuron coverage takes an input set in and measures what proportion of the overall network’s logic is tested by the input set, with the goal of finding corner cases in DNN. In my thesis, I first propose a method for computing the volume of the star input set, which is a representation of polytopes used in reachability analysis. By computing the volume, we can find a general idea of how much of the overall input space is tested for by a given input set. Secondly, I propose a method for computing neuron coverage for reachable sets. Currently, neuron coverage is only used to test individual inputs, as opposed to continuous input sets, but in this paper, we extend the definition so that we can compute neuron coverage on sets such as the star set. We implement these techniques in the Neural Network Verification (NNV) toolbox, and test it on examples, such as the Aircraft Collision Avoidance System for unmanned aircraft (ACAS Xu).
    Show full item record

    Files in this item

    Icon
    Name:
    YU-THESIS-2020.pdf
    Size:
    574.8Kb
    Format:
    PDF
    View/Open

    This item appears in the following collection(s):

    • Electronic Theses and Dissertations

    Connect with Vanderbilt Libraries

    Your Vanderbilt

    • Alumni
    • Current Students
    • Faculty & Staff
    • International Students
    • Media
    • Parents & Family
    • Prospective Students
    • Researchers
    • Sports Fans
    • Visitors & Neighbors

    Support the Jean and Alexander Heard Libraries

    Support the Library...Give Now

    Gifts to the Libraries support the learning and research needs of the entire Vanderbilt community. Learn more about giving to the Libraries.

    Become a Friend of the Libraries

    Quick Links

    • Hours
    • About
    • Employment
    • Staff Directory
    • Accessibility Services
    • Contact
    • Vanderbilt Home
    • Privacy Policy