Deceit Detection: Identification of Presenter's Subjective Doubt Using Affective Observation Neural Network Analysis

Abstract

We live in a world surrounded with 'fake news' and manipulated information, so a system assisting people with knowing what information to trust would be beneficial. Our research investigates situations where the presenters themselves have doubts about the information they are delivering, and we detect this via advanced affective computing techniques. To this end we examine the physiological foundations for observer recognition of the doubt effect: the subjective belief or disbelief of a presenter in some information he or she is presenting. Firstly, we construct stimulus videos that display presenters delivering information about which we manipulate their degree of doubt. We then show these stimuli to observers, and record four of their physiological signals. We find that a generalised neural network trained with physiological features is more accurate in differentiating the presenters' doubt/manipulated belief when compared with the same observers' own conscious judgments. The affective recognition performance improves when we analyse the physiological signals using multi-task learning techniques to train personalised and group personalised neural networks. The ability to recognise this doubt effect derives from observers' fundamental emotional reactions to the viewed stimuli, reflected in their physiological responses, and learnt by our neural networks. We believe this system using observer physiological signals collected in real life could reveal accurate and hidden audience distrust, which could in turn lead to enhanced truthfulness in future public- presented statements.