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Jayamohan, M.
- A Hybrid Copy-Move Forgery Detection Technique Using Regional Similarity Indices
Abstract Views :196 |
PDF Views:116
Authors
Affiliations
1 Sree Buddha College of Engineering for Women, Kerala, IN
2 College of Applied Science, Adoor, IN
1 Sree Buddha College of Engineering for Women, Kerala, IN
2 College of Applied Science, Adoor, IN
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 7, No 4 (2015), Pagination: 127-134Abstract
Different methods have been experimented for processing and detecting forgery in digital images. Image forgery involves various activities like copy-move forgery, image slicing, retouching, morphing etc. In copy-move forgery a portion within the image is copied and pasted on another part of the same image, generally to conceal or enhance certain portions of the image. This paper proposes a copy-move forgery detection using local fractal dimension and structural similarity indices. The image is classified into different texture regions based on the local fractal dimension. Forgery checking is thus confined to be among the portions within a region. Structural similarity index measure is applied to each block pair in each region to localize the forged portion. Experimental results prove that this hybrid method can effectively detect such kind of image tampering with minimum false positives.Keywords
Copy-Move Forgery Detection, Image Forensics, Local Fractal Dimension & structural Similarity Index.Full Text
- Dynamic Domain Classification for Fractal Image Compression
Abstract Views :176 |
PDF Views:109
Authors
K. Revathy
1,
M. Jayamohan
1
Affiliations
1 Department of Computer Science, University of Kerala, Thiruvananthapuram, Kerala, IN
1 Department of Computer Science, University of Kerala, Thiruvananthapuram, Kerala, IN
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 4, No 2 (2012), Pagination: 95-102Abstract
Fractal image compression is attractive except for its high encoding time requirements. The image is encoded as a set of contractive affine transformations. The image is partitioned into non-overlapping range blocks, and a best matching domain block larger than the range block is identified. There are many attempts on improving the encoding time by reducing the size of search pool for range-domain matching. But these methods are attempting to prepare a static domain pool that remains unchanged throughout the encoding process. This paper proposes dynamic preparation of separate domain pool for each range block. This will result in significant reduction in the encoding time. The domain pool for a particular range block can be decided based upon a parametric value. Here we use classification based on local fractal dimension.Keywords
Fractal Image Compression, Dynamic Domain Pool, RMS, Fractal Dimension.Full Text