Name
Title | ||
|---|---|---|
Identifying natural images and computer generated graphics based on binary similarity measures of PRNU. |
Abstract | ||
|---|---|---|
Aiming at the identification of natural images and computer generated graphics, an image source pipeline forensics method based on binary similarity measures of PRNU (photo response non-uniformity) is proposed. As PRNU is a unique attribute of natural images, binary similarity measures of PRNU are used to represent the differences between natural images and computer generated graphics. Binary Kullback-Leibler distance, binary minimum histogram distance, binary absolute histogram distance and binary mutual entropy are calculated from PRNU in RGB three channels. With a total of 36 dimensions of features, LIBSVM is used for classification. Experimental results and analysis indicate that it can achieve an average identification accuracy of 99.83%, and the capability of identifying natural images and computer generated graphics is balanced. Meanwhile, it is robust against JPEG compression, rotation and additive noise. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11042-017-5101-3 | Multimedia Tools Appl. |
Keywords | Field | DocType |
Image source identification, Binary similarity measures, Photo response non-uniformity noise (PRNU) | Graphics,Histogram,Computer vision,Pattern recognition,Computer science,Communication channel,Artificial intelligence,RGB color model,Jpeg compression,Binary number | Journal |
Volume | Issue | ISSN |
78 | 1 | 1573-7721 |
Citations | PageRank | References |
2 | 0.37 | 24 |
Authors | ||
3 | ||