Abstract
In this paper, we propose an image splicing detecting method using the characteristic function moments for the inter-scale co-occurrence matrix in the wavelet domain. We construct the co-occurrence matrices by using a pair of wavelet difference values across inter-scale wavelet subbands. In this process, we do not adopt the thresholding operation to prevent information loss. We extract the high-order characteristic function moments of the two-dimensional joint density function generated by the inter-scale co-concurrent matrices in order to detect image splicing forgery. Our method can be applied regardless of the color or gray image dataset using only luminance component of an image. By performing experimental simulations, we demonstrate that the proposed method achieves good performance in splicing detection. Our results show that the detection accuracy was greater than 95 % on average with well-known four splicing detection image datasets.
Highlights
In recent years, with the increasing popularity and usage of digital cameras, together with the development of image editing technologies, it has become much easier for people with minimal expertise to edit image data
The development of reliable image forgery detection methods is important to enable us to determine the authenticity of the images
We propose an efficient image splicing detection algorithm by using both local and global statistical features in the wavelet domain
Summary
With the increasing popularity and usage of digital cameras, together with the development of image editing technologies, it has become much easier for people with minimal expertise to edit image data. A 2D Markov model was applied in the DCT domain and the discrete Meyer wavelet transform domain, and the crossdomain features were considered as the final discriminative features for classification purposes This scheme achieved a detection rate of 93.36 % on Colombia gray image dataset; up to 14,240 features were required. Muhammad et al proposed an imposing image forgery detection method based on a steerable pyramid transform and local binary pattern with feature reduction [17] This method demonstrated the best performance to rate of 97.33 % on CASIA2 dataset [20]. 2016, an image slicing detection algorithm [19] using inter-scale joint characteristic function moments in the wavelet domain This algorithm showed that the discriminability for slicing detection increased through the maximization process, and threshold expansion reduces the information loss caused by the coefficient thresholding that is used to restrict the number of Markov features.
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