The re-capturing of video content poses significant challenges to algorithms in the fields of video forensics, watermarking, and near-duplicate detection. Using a camera to record a video from a display introduces a variety of artifacts, such as geometric distortions, luminance transformations, and temporal aliasing. A deep understanding of the causes and effects of such phenomena is required for their simulation, and for making the affected algorithms more robust. In this paper, we provide a detailed model of the temporal effects in re-captured video. Such effects typically result in the re-captured frames being a blend of the original video's source frames, where the specific blend ratios are difficult to predict. Our proposed parametric model captures the temporal artifacts introduced by interactions between the video renderer, display device, and camera. The validity of our model is demonstrated through experiments with real re-captured videos containing specially marked frames.

To support the development of any system that includes the generation and evaluation of camcorder copies, as well as to provide a common benchmark for robustness against camcorder copies, we present a tool to simulate digital video re-acquisition using a digital video camera. By resampling each video frame, we simulate the typical artifacts occurring in a camcorder copy: geometric modifications (aspect ratio changes, cropping, perspective and lens distortion), temporal sampling artifacts (due to different frame rates, shutter speeds, rolling shutters, or playback), spatial and color subsampling (rescaling, filtering, Bayer color filter array), and processing steps (automatic gain control, automatic white balance). We also support the simulation of camera movement (e.g., a hand-held camera) and background insertion. Furthermore, we allow for an easy setup and calibration of all the simulated artifacts, using sample/reference pairs of images and videos. Specifically temporal subsampling effects are analyzed in detail to create realistic frame blending artifacts in the simulated copies. We carefully evaluated our entire camcorder simulation system and found that the models we developed describe and match the real artifacts quite well.

In 1998, Petitcolas et al. proposed StirMark [14] as a benchmark for image watermarking schemes. The main idea was to introduce a re-sampling process that mimics the analog process of printing and scanning a watermarked image. For digital video, the corresponding concept is a camcorder copy, where a video displayed on a screen is (digitally) recorded using a video camera. As most commercial video streaming systems (VOD, IPTV) and offline distribution (Blu-ray, HDDs for cinemas) are strongly protected by means of DRM, filming a display is actually a relevant use case and a requirement for robust video watermarking systems to survive.

We present a novel system for automatically detecting salient image regions in stereoscopic videos. Our proposed algorithm considers information based on three dimensions: salient colors in individual frames, salient information derived from camera and object motion, and depth saliency. These three components are dynamically combined into one final saliency map based on the reliability of the individual saliency detectors. Such a combination allows using more efficient algorithms even if the quality of one detector degrades. For example, we use a computationally efficient stereo correspondence algorithm that might cause noisy disparity maps for certain scenarios. In this case, however, a more reliable saliency detection algorithm such as the image saliency is preferred. To evaluate the quality of the saliency detection, we created modified versions of stereoscopic videos with the non-salient regions blurred. Having users rate the quality of these videos, the results show that most users do not detect the blurred regions and that the automatic saliency detection is very reliable.

In the efforts to fight piracy of high-valued media content, forensic digital watermarking as a passive content security scheme is a potential alternative to current, restrictive approaches like DRM. In this paper, we present a novel watermarking scheme for videos based on affine geometric transformations. Frames can be modified in an imperceptible manner by applying a small, global rotation, translation, or zooming, which can be detected later on by comparison with the originals. To compensate geometric distortions that have been introduced while a video travels down legal as well as illegal distribution chains, a spatio-temporal synchronization is performed using our video registration toolkit application. To evaluate our approach, we compare it with several other schemes regarding the robustness against common attacks, including camcorder capture.

Virtually every video watermarking technology can benefit from comparison with the original content. For non-blind schemes it is fundamental; for others it is an improvement to increase the watermark’s signal-to-noise ratio by subtract-ing the content that is often noise to the detector. A direct frame-by-frame comparison of the videos is not possible due to the fact that illegal copies of videos usually differ signifi-cantly from their originals caused by different spatial resolu-tion or frame rates, geometric distortions from capturing, or targeted attacks. In this paper, we present a software tool that enables the semi-automatic temporal and spatial syn-chronization of frames and pixels of two similar videos. This process is called registration. We put our focus on utilizing human capabilities with the smallest possible effort, to allow a high overall performance and precision of the registration. An efficient graphical user interface supports the users and visualizes the results of all steps. In addition, we specifically distinguish digitally reproduced copies from analog (cam-corded) copies in which two or more frames are blended into a new frame.