  In recent years, increasing deployment of face recognition technology in
security-critical settings, such as border control or law enforcement, has led
to considerable interest in the vulnerability of face recognition systems to
attacks utilising legitimate documents, which are issued on the basis of
digitally manipulated face images. As automated manipulation and attack
detection remains a challenging task, conventional processes with human
inspectors performing identity verification remain indispensable. These
circumstances merit a closer investigation of human capabilities in detecting
manipulated face images, as previous work in this field is sparse and often
concentrated only on specific scenarios and biometric characteristics.
  This work introduces a web-based, remote visual discrimination experiment on
the basis of principles adopted from the field of psychophysics and
subsequently discusses interdisciplinary opportunities with the aim of
examining human proficiency in detecting different types of digitally
manipulated face images, specifically face swapping, morphing, and retouching.
In addition to analysing appropriate performance measures, a possible metric of
detectability is explored. Experimental data of 306 probands indicate that
detection performance is widely distributed across the population and detection
of certain types of face image manipulations is much more challenging than
others.
