In this presentation, we introduce a novel computer vision based attack that automatically discloses inputs on a touch enabled device. Our spying camera, including Google Glass, can take a video of the victim tapping on the touch screen and automatically recognize more than 90% of the tapped passcodes from three meters away, even if our naked eyes cannot see those passcodes or anything on the touch screen. The basic idea is to track the movement of the fingertip and use the fingertip's relative position on the touch screen to recognize the touch input. We carefully analyze the shadow formation around the fingertip, apply the optical flow, deformable part-based model (DPM) object detector, k-means clustering and other computer vision techniques to automatically track the touching fingertip and locate the touched points. Planar homography is then applied to map the estimated touched points to a software keyboard in a reference image. Our work is substantially different from related work on blind recognition of touch inputs. We target passcodes where no language model can be applied to correct estimated touched keys. We are interested in scenarios such as conferences and similar gathering places where a Google Glass, webcam, or smartphone can be used for a stealthy attack. Extensive experiments were performed to demonstrate the impact of this attack. As a countermeasure, we design a context aware Privacy Enhancing Keyboard (PEK) which pops up a randomized keyboard on Android systems for sensitive information such as password inputs and shows a conventional QWERTY keyboard for normal inputs.