field effect, soft-shadow, caustics effect (i.e., the specular light pattern seen near a glass when the glass is illuminated), and so on. 2.  PIM images from the personal collections (Personal): The Personal  set consists of two parts, i.e., 800 images from the authors' personal collections (Personal Columbia) and 400 images from the personal collection of Philip Greenspun (Personal Greenspun). The reason for including images from Greenspun's collection is to increase the diversity of the  Personal  set in terms of the image content, the camera models and the photographer styles. The Personal Greenspun set are mainly travel images with content such as indoor, outdoor, people, objects, building and so on. Whereas the Personal Columbia set are acquired by the authors using the professional single-len-reflex (SLR) Canon 10D and Nikon D70. It has content diversity in terms of indoor or outdoor scenes, natural or artificial objects, and lighting conditions of day time, dusk or night time. See Figure 2(b). 3.  800 PIM from Google Image Search (Google): These images are the search results based on the keywords that match the categories within the PRCG  set. The keywords are such as architecture, people, scenery, indoor, forest, statue and so on. 4.  800 photographed PRCG (Recaptured CG): These are the pho- tograph of the screen display of the images from the PRCG  set. Com- puter graphics are displayed on a 17-inch (gamma linearized) LCD monitor screen with a display resolution of 1280x1024 and photographed by a Canon G3 digital camera. The acquisition is conducted in a dark room in order to reduce the reflections from the ambient scene. The rationale of collecting two different sets of PIM is the following: the Google  set has a diverse image content and involves more types of cameras, photographer styles and lighting conditions but the ground truth may not be reliable, whereas the Personal  set has reliable sources but it has limited diversity in camera and photographer style factors.  On the other hand, based on the two-level definitions of image authenticity, i.e., the imaging- process authenticity and the scene, as introduced in [21], we should be able to restore the imaging-process authenticity of the PRCG by recapturing them using a camera. Therefore, we produce the Recaptured PRCG  image set for evaluating how much the scene authenticity can be captured by a classifier. 6