Spotting fully-synthetic facial images via local camera surface frames

The actual capacity to AI-generate realistic fully synthetic images is day-by-day improving and this is particularly true for pictures representing human faces that appear indistinguishable from real people. This poses the crucial need to develop instruments able to discern between true and do not existing people by detecting some eventual inconsistencies embedded within the images during the generation process. The main difference between a pristine picture and a deepfake generated one is that, in the second case, there has not been an effective camera acquisition; so all the various interrelationships among the elements belonging to the scene (lights, reflectance, object respective positions in the 3D space) are not taken by the real world in that precise time instant but just artificially reproduced. According to such consideration, in this work, we introduce local camera surface frames as a possible mean to represent these specific environmental characteristics in order to highlight differences. The experimental analysis carried out has witnessed that this feature can grant a very high level of accuracy and a significant degree of generalization.