Non-Overlapping Block-based Parametric Forgery Detection Model

Sharma, Kusam; Abrol, Pawanesh

doi:10.5120/ijca2016907773

Cited by 1 publication

(1 citation statement)

References 19 publications

(12 reference statements)

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“…Some block-based methods require the image to be preprocessed before any further analysis. In [5,15,18,22,23] an image was converted to grayscale using the luminance formula Y = 0.299R + 0.587G + 0.114B , where R, G, and B represent the red, green, and blue luminance respectively, of the colour image. Each color is represented by a vector which provides the brightness intensity, for example 0 to 255 for 8-bit images.…”

Section: Preprocessingmentioning

confidence: 99%

Blind copy-move forgery detection using SVD and KS test

2020

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In this paper, we present a new copy-move forgery detection method based on singular value decomposition (SVD) and the Kolmogorov Smirnov (KS) test. This work introduces a new method of detecting copy-move forgery in images with accuracy up to the pixel level using only 4 features per image block. The proposed method consists of three steps. First, an image is partitioned into blocks of size 16 × 16. Second, image features are extracted from each block using steerable pyramid and SVD transforms. Finally, the extracted features are sorted lexicographically and matched using the KS test. The performance of the proposed method is evaluated using the CoMoFoD database. Four post-processing techniques are considered, namely brightness change, contrast adjustment, color reduction, and image blurring. This method achieved a high precision of more than 95% for 3 of the 4 post-processing techniques. The fourth post-processing (i.e., image blurring), we achieved a precision of 75% which is considerably high for such forgery. In addition, the proposed method outperformed published methods when the images were subjected to brightness change, contrast adjustment, color reduction and image blurring. Finally, the performance of the proposed algorithm shown to provide better precision using fewer features compared to several well-known techniques in the literature.

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