Robust Hashing Based Video Content Authentication

2013 ◽  
Vol 10 (18) ◽  
pp. 5919-5926
Author(s):  
Qiang Ma
Keyword(s):  
Video Content ◽  
Content Authentication ◽  
Video Content Authentication

scholarly journals Perceptual hashing method for video content authentication with maximized robustness

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Qiang Ma ◽  
Ling Xing
Keyword(s):  
Optimization Problem ◽  
Optimization Method ◽  
Perceptual Content ◽  
Video Content ◽  
Content Authentication ◽  
Constrained Optimization Problem ◽  
Video Hashing ◽  
Perceptual Hashing ◽  
Video Content Authentication ◽  
Perceptual Video Hashing

AbstractPerceptual video hashing represents video perceptual content by compact hash. The binary hash is sensitive to content distortion manipulations, but robust to perceptual content preserving operations. Currently, boundary between sensitivity and robustness is often ambiguous and it is decided by an empirically defined threshold. This may result in large false positive rates when received video is to be judged similar or dissimilar in some circumstances, e.g., video content authentication. In this paper, we propose a novel perceptual hashing method for video content authentication based on maximized robustness. The developed idea of maximized robustness means that robustness is maximized on condition that security requirement of hash is first met. We formulate the video hashing as a constrained optimization problem, in which coefficients of features offset and robustness are to be learned. Then we adopt a stochastic optimization method to solve the optimization. Experimental results show that the proposed hashing is quite suitable for video content authentication in terms of security and robustness.

A Survey on Video Watermarking

2011 ◽  
pp. 472-492
Author(s):  
Shiguo Lian
Keyword(s):  
Copyright Protection ◽  
Error Resilience ◽  
Video Watermarking ◽  
Video Content ◽  
Content Authentication ◽  
Information Redundancy ◽  
Performance Requirement ◽  
Customer Information ◽  
Open Issues ◽  
Information Integrity

Video watermarking technique embeds some information into videos by modifying video content slightly. The embedded information, named watermark, may be ownership information, customer information, integrity information, redundancy information, and so forth. Thus, this technique can be used for copyright protection, piracy tracing, content authentication, advertisement surveillance, error resilience, and so forth. In this chapter, we give an overview on video watermarking technology, including its architecture, performance requirement, typical algorithms, hot topics, and open issues.

Amazons Kampf um das Wohnzimmer

2016 ◽  
Vol 13 (3) ◽  
pp. 37-39
Author(s):  
Ralf Kaumanns
Keyword(s):  
Streaming Media ◽  
Video On Demand ◽  
Video Content ◽  
On Demand

Der Kampf um das Wohnzimmer ist voll entbrannt. Eine Reihe von Anbietern versuchen Streaming Media-Dienste im deutschen Markt zu etablieren. Amazon hat sich mit seiner Strategie eine marktführende Rolle erarbeiten können. Laut einer Analyse von Goldmedia¹ besitzt Amazon mittlerweile einen Anteil im Video-On-Demand-Markt von 38,9%, deutlich vor Wettbewerbern wie Apple, Maxdome, Google oder Netflix. Der Erfolg kommt nicht von ungefähr. Der Grund liegt vor allem in einer umfassenden Strategie rund um das Thema Bewegtbild und Video Content. Im Kampf um das Wohnzimmer haben selbst große und finanzkräftige Wettbewerber einen schweren Stand, um mit umfassend gebündelten Angeboten Schritt zu halten.

A Novel Verification Scheme for Watermarking Based Image Content Authentication Systems

2008 ◽  
Vol 67 (19) ◽  
pp. 1777-1790 ◽  
Author(s):  
C. Cruz-Ramos ◽  
R. Reyes-Reyes ◽  
J. Mendoza-Noriega ◽  
Mariko Nakano-Miyatake ◽  
Hector Manuel Perez-Meana
Keyword(s):  
Content Authentication ◽  
Image Content ◽  
Verification Scheme

Detecting “DeepFakes” in H.264 Video Data Using Compression Ghost Artifacts

2020 ◽  
Vol 2020 (4) ◽  
pp. 116-1-116-7
Author(s):  
Raphael Antonius Frick ◽  
Sascha Zmudzinski ◽  
Martin Steinebach
Keyword(s):  
Image Forensics ◽  
Video Data ◽  
Experimental Results ◽  
Video Sequences ◽  
The Internet ◽  
Video Content ◽  
High Quality ◽  
The Public

In recent years, the number of forged videos circulating on the Internet has immensely increased. Software and services to create such forgeries have become more and more accessible to the public. In this regard, the risk of malicious use of forged videos has risen. This work proposes an approach based on the Ghost effect knwon from image forensics for detecting forgeries in videos that can replace faces in video sequences or change the mimic of a face. The experimental results show that the proposed approach is able to identify forgery in high-quality encoded video content.

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