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Detection of Upscale-Crop and Partial Manipulation in Surveillance Video Based on Sensor Pattern Noise

1
Department of Computer Science, Korea Advanced Institute of Science and Technology, 291 Daehak-Ro, Yuseong-Gu, Daejon, Korea
2
Department of Computer and Software Engineering, Kumoh National Institute of Technology, Yangho-dong, Gumi, Gyeongbuk, Korea
*
Author to whom correspondence should be addressed.
Sensors 2013, 13(9), 12605-12631; https://doi.org/10.3390/s130912605
Received: 25 July 2013 / Revised: 20 August 2013 / Accepted: 6 September 2013 / Published: 18 September 2013
(This article belongs to the Section Physical Sensors)
In many court cases, surveillance videos are used as significant court evidence. As these surveillance videos can easily be forged, it may cause serious social issues, such as convicting an innocent person. Nevertheless, there is little research being done on forgery of surveillance videos. This paper proposes a forensic technique to detect forgeries of surveillance video based on sensor pattern noise (SPN). We exploit the scaling invariance of the minimum average correlation energy Mellin radial harmonic (MACE-MRH) correlation filter to reliably unveil traces of upscaling in videos. By excluding the high-frequency components of the investigated video and adaptively choosing the size of the local search window, the proposed method effectively localizes partially manipulated regions. Empirical evidence from a large database of test videos, including RGB (Red, Green, Blue)/infrared video, dynamic-/static-scene video and compressed video, indicates the superior performance of the proposed method. View Full-Text
Keywords: digital image forensic; sensor pattern noise; forgery detection; surveillance video forgery; MACE-MRH correlation filter digital image forensic; sensor pattern noise; forgery detection; surveillance video forgery; MACE-MRH correlation filter
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MDPI and ACS Style

Hyun, D.-K.; Ryu, S.-J.; Lee, H.-Y.; Lee, H.-K. Detection of Upscale-Crop and Partial Manipulation in Surveillance Video Based on Sensor Pattern Noise. Sensors 2013, 13, 12605-12631. https://doi.org/10.3390/s130912605

AMA Style

Hyun D-K, Ryu S-J, Lee H-Y, Lee H-K. Detection of Upscale-Crop and Partial Manipulation in Surveillance Video Based on Sensor Pattern Noise. Sensors. 2013; 13(9):12605-12631. https://doi.org/10.3390/s130912605

Chicago/Turabian Style

Hyun, Dai-Kyung, Seung-Jin Ryu, Hae-Yeoun Lee, and Heung-Kyu Lee. 2013. "Detection of Upscale-Crop and Partial Manipulation in Surveillance Video Based on Sensor Pattern Noise" Sensors 13, no. 9: 12605-12631. https://doi.org/10.3390/s130912605

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