The super-recogniser advantage extends to the detection of digitally manipulated faces

Face recognition by human officials remains the predominant method of identity verification in security-critical contexts (e.g., passport renewal, border control). The integrity of this process can be compromised by sophisticated fraud attacks using digitally manipulated face images. Therefore, in this study we examine whether human observers can robustly detect digitally manipulated passport photos and whether super-recognisers (SRs), individuals who excel at identity recognition, outperform typical recogniser controls. Using two face manipulation detection tasks (DFMD1, DFMD2), untrained participants were asked to decide whether ‘suspected’ passport photos had been digitally manipulated. Compared to controls, SRs demonstrated significantly superior discrimination of manipulated and unmanipulated images. Effect sizes were strong. This was not due to ‘speed-accuracy’ trade-offs. Instead, performances on three face aptitude tests predicted DFMD1 and DMD2 sensitivity (d’), and with self-rated face recognition aptitude, and faster response times, accounted for approximately 26% of the variance in manipulated image detection sensitivity on the more ecologically valid DFMD2. Despite increasing sophistication in deceptive digital face manipulation techniques, the results suggest that utility should be enhanced by employing human SRs, to detect them. Further cognitive and applied implications of these findings are discussed.