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 spoofing attacks utilizing legitimate documents based on digitally manipulated face images. As automated attack detection remains a computationally demanding task with operational limitations regarding unconstrained environments, conventional processes with officers performing identity verification remain indispensable, in part attributable to the remarkably low impact of difficult viewing conditions. These circumstances merit a closer look at human capabilities in detecting manipulated face images, as previous work in this field is sparse and often concentrated on distinct scenarios and characteristics. This paper presents a flexible web-based framework and application providing functionality to design and conduct remote visual discrimination experiments on the basis of principles adopted from the field of psychophysics and subsequently discusses an exploratory trial sequence with the aim of examining human proficiency in detecting different types of digitally manipulated face images, specifically face swap, morphing and retouching. In addition to analyzing limitations and presenting overall performance measures suggestive of independence from domain-specific experience, a possible metric of detectability is considered by means of estimating a psychometric function from acquired experimental data.