Digital Image Tamper Detection Technique Based on Spectrum Analysis of CFA Artifacts

Existence of mobile devices with high performance cameras and powerful image processing applications eases the alteration of digital images for malicious purposes. This work presents a new approach to detect digital image tamper detection technique based on CFA artifacts arising from the differences...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2018-08, Vol.18 (9), p.2804
Main Authors: González Fernández, Edgar, Sandoval Orozco, Ana Lucila, García Villalba, Luis Javier, Hernandez-Castro, Julio
Format: Article
Language:English
Subjects:
Algorithms
Bayer Filter
CFA artifacts
Color
Color Filter Array
Computer science
Digital cameras
Digital imaging
Digitization
Discrete Cosine Transform
Electric noise
Filtration
Forgery
Image detection
Image Forensics
Image processing
image tamper detection
International conferences
Light
Multimedia
Pixels
Sensors
Signal processing
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Summary:Existence of mobile devices with high performance cameras and powerful image processing applications eases the alteration of digital images for malicious purposes. This work presents a new approach to detect digital image tamper detection technique based on CFA artifacts arising from the differences in the distribution of acquired and interpolated pixels. The experimental evidence supports the capabilities of the proposed method for detecting a broad range of manipulations, e.g., copy-move, resizing, rotation, filtering and colorization. This technique exhibits tampered areas by computing the probability of each pixel of being interpolated and then applying the DCT on small blocks of the probability map. The value of the coefficient for the highest frequency on each block is used to decide whether the analyzed region has been tampered or not. The results shown here were obtained from tests made on a publicly available dataset of tampered images for forensic analysis. Affected zones are clearly highlighted if the method detects CFA inconsistencies. The analysis can be considered successful if the modified zone, or an important part of it, is accurately detected. By analizing a publicly available dataset with images modified with different methods we reach an 86% of accuracy, which provides a good result for a method that does not require previous training.
ISSN:1424-8220
1424-8220
DOI:10.3390/s18092804