Multimedia Forensics Is Not Computer Forensics

Computational Forensics - Lecture Notes in Computer Science ◽

10.1007/978-3-642-03521-0_9 ◽

2009 ◽

pp. 90-103 ◽

Cited By ~ 14

Author(s):

Rainer Böhme ◽

Felix C. Freiling ◽

Thomas Gloe ◽

Matthias Kirchner

Keyword(s):

Computer Forensics ◽

Multimedia Forensics

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Research of distributed and autonomous computer forensics system

Journal of Computer Applications ◽

10.3724/sp.j.1087.2008.02485 ◽

2009 ◽

Vol 28 (10) ◽

pp. 2485-2487

Author(s):

Xi-ai YAN ◽

Jin-min YANG ◽

Wei-dong CHANG

Keyword(s):

Computer Forensics

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CNN-Based Video Codec Classifier For Multimedia Forensics

10.1109/icip42928.2021.9506020 ◽

2021 ◽

Author(s):

Rodrigo Pessoa ◽

Anil Kokaram ◽

Francois Pitie ◽

Mark Sugrue

Keyword(s):

Video Codec ◽

Multimedia Forensics

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Effective splicing localization based on noise level inconsistencies

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189861 ◽

2021 ◽

pp. 1-11

Author(s):

P. N. R. L. Chandra Sekhar Author ◽

T. N. Shankar Author

Keyword(s):

Digital Technology ◽

Social Networking Sites ◽

Noise Level ◽

Research Work ◽

Multimedia Content ◽

Noise Levels ◽

Multimedia Forensics ◽

The Past ◽

Photo Editing ◽

State Of Art

In the era of digital technology, it becomes easy to share photographs and videos using smartphones and social networking sites to their loved ones. On the other hand, many photo editing tools evolved to make it effortless to alter multimedia content. It makes people accustomed to modifying their photographs or videos either for fun or extracting attention from others. This altering brings a questionable validity and integrity to the kind of multimedia content shared over the internet when used as evidence in Journalism and Court of Law. In multimedia forensics, intense research work is underway over the past two decades to bring trustworthiness to the multimedia content. This paper proposes an efficient way of identifying the manipulated region based on Noise Level inconsistencies of spliced mage. The spliced image segmented into irregular objects and extracts the noise features in both pixel and residual domains. The manipulated region is then exposed based on the cosine similarity of noise levels among pairs of individual objects. The experimental results reveal the effectiveness of the proposed method over other state-of-art methods.

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