Evaluating the efficacy of RGB-D cameras for surveillance
RGB-D cameras have enabled real-time 3D video processing for numerous computer vision applications, especially for surveillance type applications. In this paper, we first present a real-time anti-forensic 3D object stream manipulation framework to capture and manipulate live RBG-D data streams to create realistic images/videos showing individuals performing activities they did not actually do. The framework uses computer vision and graphics methods to render photorealistic animations of live mesh models captured using the camera. Next, we conducted a visual inspection of the manipulated RGB-D streams (just like security personnel would do) by users who are computer vision and graphics scientists. The study shows that it was significantly difficult to distinguish between the real or reconstructed rendering of such 3D video sequences, thus clearly showing the potential security risk involved. Finally, we investigate the efficacy of forensic approaches for detecting such manipulations.