Anti-Forensics Contrast Enhancement Detection (AFCED) Technique in Images Based on Laplace Derivative Histogram

Histogram based forensic techniques to detect contrast enhancement, after an initial success, became unreliable due to the development of targeted anti-forensic attacks. These attacks eliminate statistical footprints left by enhancement on the histogram, making the image modifications undetectable....

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Bibliographic Details
Published in:Mobile networks and applications 2019-08, Vol.24 (4), p.1174-1180
Main Authors: Bharathiraja, S, Rajesh, Kanna B
Format: Article
Language:English
Subjects:
Algorithms
Anomalies
Communications Engineering
Computer Communication Networks
Electrical Engineering
Engineering
Forensic sciences
Histograms
Image contrast
Image detection
Image enhancement
IT in Business
Networks
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Summary:Histogram based forensic techniques to detect contrast enhancement, after an initial success, became unreliable due to the development of targeted anti-forensic attacks. These attacks eliminate statistical footprints left by enhancement on the histogram, making the image modifications undetectable. Further, these techniques in-spite of being successful in making histograms of the enhanced image appear more natural, they themselves introduce anomalies in the spatial domain. This paper presents a novel algorithm that, for the first time, exploits the statistical anomalies through the Laplace modeling of the derivative histogram to detect the anti-forensic contrast enhancement. Experimental results demonstrate that the proposed algorithm is effective in detecting contrast enhancements executed both by regular as well as anti-forensics techniques.
ISSN:1383-469X
1572-8153
DOI:10.1007/s11036-019-01255-1