5 research outputs found
Mitigation of H.264 and H.265 Video Compression for Reliable PRNU Estimation
The photo-response non-uniformity (PRNU) is a distinctive image sensor
characteristic, and an imaging device inadvertently introduces its sensor's
PRNU into all media it captures. Therefore, the PRNU can be regarded as a
camera fingerprint and used for source attribution. The imaging pipeline in a
camera, however, involves various processing steps that are detrimental to PRNU
estimation. In the context of photographic images, these challenges are
successfully addressed and the method for estimating a sensor's PRNU pattern is
well established. However, various additional challenges related to generation
of videos remain largely untackled. With this perspective, this work introduces
methods to mitigate disruptive effects of widely deployed H.264 and H.265 video
compression standards on PRNU estimation. Our approach involves an intervention
in the decoding process to eliminate a filtering procedure applied at the
decoder to reduce blockiness. It also utilizes decoding parameters to develop a
weighting scheme and adjust the contribution of video frames at the macroblock
level to PRNU estimation process. Results obtained on videos captured by 28
cameras show that our approach increases the PRNU matching metric up to more
than five times over the conventional estimation method tailored for photos
Content based video copy detection using motion vectors
Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 57-61.In this thesis, we propose a motion vector based Video Content Based Copy
Detection (VCBCD) method. Detecting the videos violating the copyright of the
owner comes into question by growing broadcasting of digital video on different
media. Unlike watermarking methods in VCBCD methods, the video itself is
considered as a signature of the video and representative feature parameters are
extracted from a given video and compared with the feature parameters of a test
video. Motion vectors of image frames are one of the signatures of a given video.
We first investigate how well the motion vectors describe the video.
We use Mean value of Magnitudes of Motion Vectors (MMMV) and Mean
value of Phases of Motion Vectors (MPMV) of macro blocks, which are the main
building blocks of MPEG-type video coding methods. We show that MMMV
and MPMV plots may not represent videos uniquely with little motion content
because the average of motion vectors in a given frame approaches zero.
To overcome this problem we calculate the MMMV and MPMV graphs in
a lower frame rate than the actual frame rate of the video. In this way, the
motion vectors may become larger and as a result robust signature plots are obtained. Another approach is to use the Histogram of Motion Vectors (HOMV)
that includes both MMMV and MPMV information.
We test and compare MMMV, MPMV and HOMV methods using test videos
including copies and the original movies.Taşdemir, KasımM.S