Bertrand Chupeau

A Video Fingerprint Based on Visual Digest and Local Fingerprints

A fingerprinting design extracts discriminating features, called fingerprints. The extracted features are unique and specific to each image/video. The visual hash is usually a global fingerprinting technique with crypto-system constraints. In this paper, we propose an innovative video content identification process which combines a visual hash function and a local fingerprinting. Thanks to a visual hash function, we observe the video content variation and we detect key frames. A local image fingerprint technique characterizes the detected key frames. The set of local fingerprints for the whole video summarizes the video or fragments of the video. The video fingerprinting algorithm identifies an unknown video or a fragment of video within a video fingerprint database. It compares the local fingerprints of the candidate video with all local fingerprints of a database even if strong distortions are applied to an original content.

A framework for video forensics based on local and temporal fingerprints

This paper presents a framework for the forensic analysis of video content, for example those illegal copies of feature films proliferating in peer-to-peer networks or on bootleg DVDs. Compact and discriminant feature vectors, so called ¿fingerprints¿, are computed for a catalog of master contents. They combine global (image-based) and local (keypoint-based) signal properties extracted on representative keyframes, together with a one-dimensional signature capturing the temporal evolution of the video. We demonstrate that the extracted fingerprints not only allow accurate copy detection but also enable precise temporal and geometric alignment of candidate videos with master content. Besides making possible watermark decoding in the presence of geometric distortion, this opens the door to the reconstruction of the history of signal manipulations. The performance of the proposed scheme is assessed through a realistic copy detection and registration scenario.

Image and Video Fingerprinting: Forensic Applications

Fighting movie piracy often requires automatic content identification. The most common technique to achieve this uses watermarking, but not all copyrighted content is watermarked. Video fingerprinting is an efficient alternative solution to identify content, to manage multimedia files in UGC sites or P2P networks and to register pirated copies with master content. When registering by matching copy fingerprints with master ones, a model of distortion can be estimated. In case of in-theater piracy, the model of geometric distortion allows the estimation of the capture location. A step even further is to determine, from passive image analysis only, whether different pirated versions were captured with the same camcorder. In this paper we present three such fingerprinting-based forensic applications: UGC filtering, estimation of capture location and source identification.

Automatic estimation and compensation of geometric distortions in video copies

The proliferation of pirate copies of feature films on peer-to-peer networks arouses a great interest to countermeasures such as the insertion of (invisible) forensic marks in projected movies, to deter their illegal capture. The registration of pirate copies with the original content is however a prerequisite to the recovery of such embedded messages, as severe geometric distortions often occur in illegally camcorded contents. After a brief state-of-the-art in image registration, the paper details an algorithm for video registration, focusing on the compensation of geometric distortions. Control points are automatically extracted in original and copy pictures, followed by pre- and post-matching filtering steps to discard not relevant control points and erroneous matched pairs of control points respectively. This enables the accurate numerical estimation of an 8-parameter homographic distortion model, used to register the copy frames with the original reference grid. Such an image registration algorithm is inserted into a general video registration scheme. Results are presented on both natural and synthetic test material.

Adaptive video fingerprints for accurate temporal registration

Designing watermarks that can withstand geometric and temporal attacks is a notoriously difficult problem. The registration of a pirate content with the reference content, prior to the decoding of an embedded forensic mark, is therefore preferred. Semi-automatic registration systems, requiring the expert user to manually select and match control points through a suitable user interface, are not acceptable for content owners and do not usually provide very accurate results. A fully automated registration scheme is therefore chosen to synchronize pirate and master contents. By extracting robust and unique features, digital fingerprinting provides a relevant framework for automatic semi-blind content registration. In this paper we focus on temporal registration. In order to guarantee an accurate alignment while keeping the fingerprint size as small as possible, we propose a temporally adaptive approach. The registration scheme is designed around an adaptive motion description based on hierarchical encoding of the wavelet coefficients computed on the difference of successive frames.

Forensic characterization of camcorded movies: digital cinema vs. celluloid film prints

Digital camcording in the premises of cinema theaters is the main source of pirate copies of newly released movies. To trace such recordings, watermarking systems are exploited in order for each projection to be unique and thus identifiable. The forensic analysis to recover these marks is different for digital and legacy cinemas. To avoid running both detectors, a reliable oracle discriminating between cams originating from analog or digital projections is required. This article details a classification framework relying on three complementary features : the spatial uniformity of the screen illumination, the vertical (in)stability of the projected image, and the luminance artifacts due to the interplay between the display and acquisition devices. The system has been tuned with cams captured in a controlled environment and benchmarked against a medium-sized dataset (61 samples) composed of real-life pirate cams. Reported experimental results demonstrate that such a framework yields over 80% classification accuracy.

A sneak peek into the camcorder path

A number of technologies claim to be robust against content re-acquisition with a camera recorder e.g. water- marking and content ngerprinting. However, the benchmarking campaigns required to evaluate the impact of the camcorder path are tedious and such evaluation is routinely overlooked in practice. Due to the interaction between numerous devices, camcording displayed content modi es the video essence in various ways, including geometric distortions, temporal transforms, non-uniform and varying luminance transformations, saturation, color alteration, etc. It is necessary to clearly understand the di erent phenomena at stake in order to design ef- cient countermeasures or to build accurate simulators which mimic these e ects. As a rst step in this direction, we focus in this study solely on luminance transforms. In particular, we investigate three di erent alterations, namely: (i) the spatial non uniformity, (ii) the steady state luminance response, and (iii) the transient luminance response.

Flicker Forensics for Pirate Device Identification

Cryptography-based content protection is an efficient means to protect multimedia content during transport. Nevertheless, content is eventually decrypted at rendering time, leaving it vulnerable to piracy e.g. using a camcorder to record movies displayed on an LCD screen. Such type of piracy naturally imprints a visible flicker signal in the pirate video due to the interplay between the rendering and acquisition devices. The parameters of such flicker are inherently tied to the characteristics of the pirate devices such as the back-light of the LCD screen and the read-out time of the camcorder. In this article, we introduce a forensic methodology to estimate such parameters by analyzing the flicker signal present in pirate recordings. Experimental results clearly showcase that the accuracy of these estimation techniques offers efficient means to tell-tale which devices have been used for piracy thanks to the variety of factory settings used by consumer electronics manufacturers.

Modeling the flicker effect in camcorded videos to improve watermark robustness

Camcording a screen is a common and easy way for a pirate to capture a protected video and circumvent any cryptography-based protection system like DRM. Therefore, antipiracy methods like watermarking should be robust to this type of attack. One effect very often encountered is the flicker effect: image luminance varies periodically along the video, and scrolling stripes may also be observed. Such an effect is due to aliasing, because the higher frequency backlight of the screen is sampled at a lower rate by the camcorder. The stripe effect appears because each line of the image is captured with a small delay with CMOS sensors. We show that the amplitude of the flicker depends on the luminance of the displayed image, as well as on the luminosity of the screen and on the exposure setting of the camcorder. We propose a method to blindly estimate the flicker parameters by studying the spectrum of the camcorded video; then we show how the flicker can be selectively removed without impairing other frequential components of the video. Experiments on camcorded videos show that removing the flicker enable significant improvement in the watermarking detection performances.

Forensic characterization of pirated movies: digital cinema cam vs. optical disc rip

A large portion of pirate movies illegally shared over the Internet is either a camcorded copy of a projection in a digital cinema or is directly ripped from optical discs such as DVDs and Blu-ray discs. In this paper, we introduce a classifier that automatically discriminates between these two types of piracy in an effort to provide tools that help streamlining the whole forensic analysis process. This oracle relies on tell-tale visual artifacts that reveal the occurrence of camcording. We survey three alternate discriminative features relating to temporal flicker, color gamut, and edge orientation and detail how to combine them to obtain accurate classification. Experiments conducted on a large corpus of real pirated movies clearly demonstrate the feasibility of such classification.