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Sensor fusion for media manipulation with ambiguous hypotheses, evidence alignment, and a novel belief combination rule

Fusing sensors that each test one hypothesis from a set of ambiguous, non-exclusive hypotheses about manipulation in media using only combination rules like those in Dempster-Shafer fusion ignores information about the overlap of the sensors’ beliefs, hypotheses, and evidences. We present a novel fusion approach for sensors that test ambiguous hypotheses using semantic evidence. Our approach measures the relevance of sensors to a given hypothesis and leverages the ambiguity of hypotheses and the similarity of evidences across sensors. These factors lead to a combination rule that captures the conceptual overlaps among hypotheses, evidences, and sensors.