Many animals use concealing markings to reduce the risk of predation. These include background pattern matching (crypsis), where the coloration matches a random sample of the background and disruptive patterns, whose effectiveness has been hypothesized to lie in breaking up the body into a series of apparently unrelated objects. We have previously established the effectiveness of disruptive coloration against avian predators, using artificial moth-like stimuli with colours designed to match natural backgrounds as perceived by birds. Here, we investigate the mechanism by which disruptive patterns reduce detectability, using a computational vision model of edge detection applied to photographs of our experimental stimuli, calibrated for bird colour vision. We show that, disruptive coloration is effective by exploiting edge detection algorithms that we use to model early visual processing. Thus, 'false' edges are detected within the body rather than at its periphery, so inhibiting successful detection of the animal's body outline.