TY - JOUR
T1 - Detecting the Presence of ENF Signal in Digital Videos
T2 - A Superpixel-Based Approach
AU - Vatansever, Saffet
AU - Dirik, Ahmet Emir
AU - Memon, Nasir
N1 - Funding Information:
Manuscript received May 15, 2017; revised July 6, 2017; accepted August 8, 2017. Date of publication August 17, 2017; date of current version August 24, 2017. This work was supported in part by the DARPA and in part by the Air Force Research Laboratory under Agreement FA8750-16-2-0173. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. V. Monga. (Corresponding author: Ahmet Emir Dirik.) S. Vatansever is with the Department of Mechatronics Engineering, Bursa Technical University, Bursa 16333, Turkey, and also with Uludag University, Bursa 16059, Turkey (e-mail: saffet.vatansever@btu.edu.tr).
Publisher Copyright:
© 1994-2012 IEEE.
PY - 2017/10
Y1 - 2017/10
N2 - Electrical network frequency (ENF) instantaneously fluctuates around its nominal value (50/60 Hz) due to a continuous disparity between generated power and consumed power. Consequently, luminous intensity of a mains-powered light source varies depending on ENF fluctuations in the grid network. Variations in the luminance over time can be captured from video recordings and ENF can be estimated through content analysis of these recordings. In ENF-based video forensics, it is critical to check whether a given video file is appropriate for this type of analysis. That is, if ENF signal is not present in a given video, it would be useless to apply ENF-based forensic analysis. In this letter, an ENF signal presence detection method is introduced for videos. The proposed method is based on multiple ENF signal estimations from steady superpixels, i.e., pixels that are most likely uniform in color, brightness, and texture, and intra-class similarity of the estimated signals. Subsequently, consistency among these estimates is then used to determine the presence or absence of an ENF signal in a given video. The proposed technique can operate on video clips as short as 2 min and is independent of the camera sensor type, i.e., CCD or CMOS.
AB - Electrical network frequency (ENF) instantaneously fluctuates around its nominal value (50/60 Hz) due to a continuous disparity between generated power and consumed power. Consequently, luminous intensity of a mains-powered light source varies depending on ENF fluctuations in the grid network. Variations in the luminance over time can be captured from video recordings and ENF can be estimated through content analysis of these recordings. In ENF-based video forensics, it is critical to check whether a given video file is appropriate for this type of analysis. That is, if ENF signal is not present in a given video, it would be useless to apply ENF-based forensic analysis. In this letter, an ENF signal presence detection method is introduced for videos. The proposed method is based on multiple ENF signal estimations from steady superpixels, i.e., pixels that are most likely uniform in color, brightness, and texture, and intra-class similarity of the estimated signals. Subsequently, consistency among these estimates is then used to determine the presence or absence of an ENF signal in a given video. The proposed technique can operate on video clips as short as 2 min and is independent of the camera sensor type, i.e., CCD or CMOS.
KW - ENF detection
KW - Electrical network frequency (ENF)
KW - multimedia forensics
KW - superpixel
KW - video forensics
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U2 - 10.1109/LSP.2017.2741440
DO - 10.1109/LSP.2017.2741440
M3 - Article
AN - SCOPUS:85028499689
SN - 1070-9908
VL - 24
SP - 1463
EP - 1467
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
IS - 10
M1 - 8012515
ER -