The Odds are Odd: A Statistical Test for Detecting Adversarial Examples

We investigate conditions under which test statistics exist that can reliably detect examples, which have been adversarially manipulated in a white-box attack. These statistics can be easily computed and calibrated by randomly corrupting inputs. They exploit certain anomalies that adversarial attacks introduce, in particular if they follow the paradigm of choosing perturbations optimally under p-norm constraints. Access to the log-odds is the only requirement to defend models. We justify our approach empirically, but also provide conditions under which detectability via the suggested test statistics is guaranteed to be effective. In our experiments, we show that it is even possible to correct test time predictions for adversarial attacks with high accuracy

Machine-learning Based Automatic Formulation of Query Sequences to Improve Search

People use search engines to look up information on the Internet, using search queries related to their information needs. This disclosure describes the use of machine learning techniques, including supervised learning and reinforcement learning to train a search agent to search deeper for better, more accurate, better supported answers by interacting with the search engine. The interaction mimics strategies utilized by human experts to carry out accurate web search. The search agent can be modular, and to provide answers to a user query, performs operations such as formulation of new queries in a sequence, analysis of intermediate results, and selection of results based on a chosen success metric that can take into account factors such as accuracy, diversity, presence of justification, etc