A channelized hotelling observer for treaty-verification tasks

Binary-discrimination tasks useful to arms-control-treaty verification were performed by applying mathematical observer models used by the medical-imaging community. It is a difficult task, as the monitor needs to verify a measured object is a warhead while the host wants to prevent dissemination of sensitive information on their weapons. Inspection objects were classified by their projection data, without reconstructing an image, which often contains sensitive information. Furthermore, the models process data event-by-event, with only a scalar test statistic being updated before the observed data is purged from memory, preventing the aggregation of information which would be sensitive. Template matching models were developed from a set of calibration data on these objects, with the ultimate goal being to develop an observer model that stores only non-sensitive information sufficient for confirmation. These models were also analyzed in the presence of nuisance parameters — unknowns in the objects or imaging system that affect the projection data but aren't of interest to the discrimination task. Examples of these include object location and orientation. The Hotelling observer and channelized Hotelling observer were modeled and their benefits to information security analyzed. The Hotelling observer-the ideal linear observer when the statistics of the data are Gaussian-stores only a set of weights which are the product of the inverse average covariance matrix and difference in mean data between the two objects in the discrimination task. When testing a source, an inner product of the Hotelling weights and binned testing data is taken, resulting in a scalar that is thresholded to make a decision. If nuisance parameters are present, the mean and covariance matrix are found by averaging not only over the always present Poisson noise, but the nuisance parameter distributions as well. Hence, even if detector data is gathered from multiple realizations of an object, the Hotelling weights will be a single smeared out data set the size of the measured data. The Hotelling weights also sifts out all information other than the differences between the two objects. Hence, if the monitor was to gain access to the weights and apply the inverse to their test statistic, they could only back out a scaled version of the template, not the image. The channelized Hotelling observer-a common tool used in medical image quality assessment-was also investigated. This method drastically reduces the size of the data by applying a channeiizing matrix to the binned testing data set. An optimal set of weights for the channelized data can then be found, and the inner product between these weights and the channelized vector results in the scalar test statistic. Optimal performance is retained by optimizing the matrix to maximize the SNR2 between the test-statistic distributions for the two sources in the task. The channelized Hotelling observer gives the monitor access to multiple non-sensitive test statistics. In practice, the channelizing matrix could be implemented in hardware or software behind an information barrier. The addition of penalty terms to the channelized Hotelling observer offers further potential for this method. Individual channel performance could be penalized, leading to a large number of non-sensitive channels that the monitor can use to verify the channelization routine is working as described without gaining access to sensitive information. Noise could be added to the resulting channels, causing additional reduction of the total stored information. Finally, if the host can define what information in its object geometries is sensitive in advance, they could optimize the differences between the two objects in the task while penalizing out the sensitive information, creating a non-sensitive channeiizing matrix that could be shared with the monitor. To test these models, Monte Carlo simulations were performed with the GEANT4 toolkit. Photons were tracked from plutonium inspection objects developed by Idaho National Laboratory. We simulated the Fast-Neutron Imaging system designed by Oak Ridge National Laboratory and Sandia National Laboratories, which consists of 40×40 1cm2 liquid scintillator pixels with a plastic coded aperture. Observer models were evaluated using the area under the ROC curve. This work is supported by the Office of Defense Nuclear Nonproliferation Research and Development, Nuclear Weapon and Material Security Team. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000. (SAND2015-10391A).