Forgery Detection Scheme Using Enhanced Markov Model and LBP Texture Operator in Low Quality Images
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Image forensic is performed to check image limpidness. In this paper, a robust scheme is discussed to detect median filtering in low quality images. Detection of median filtering assists in overall image forensic. Improved spatial statistical features are extracted from the image to classify pristine and median filtered images. Image array data is rescaled to enhance the spatial statistical information. Features are extracted using Markov model on enhanced spatial statistics. Multiple difference arrays are considered in different directions for robust feature set. Further, texture operator features are combined to increase the detection accuracy and SVM binary classifier is applied to train the classification model. Experimental results are promising for images of low quality JPEG compression.
Abstract
Image forensic is performed to check image limpidness. In this paper, a robust scheme is discussed to detect median filtering in low quality images. Detection of median filtering assists in overall image forensic. Improved spatial statistical features are extracted from the image to classify pristine and median filtered images. Image array data is rescaled to enhance the spatial statistical information. Features are extracted using Markov model on enhanced spatial statistics. Multiple difference arrays are considered in different directions for robust feature set. Further, texture operator features are combined to increase the detection accuracy and SVM binary classifier is applied to train the classification model. Experimental results are promising for images of low quality JPEG compression.
참고문헌 (14)
- M. Kirchner, "On detection of median filtering in digital images", Media Forensics and Security II, 754110, pp.1~12 (2010)
- C. Chen, "Blind detection of median filtering in digital images: a difference domain based approach", IEEE Transactions on Image Processing, 22(12), pp.4699~4710 (2013)
- S. Agarwal, "SPAM revisited for median filtering detection using higher-order difference", Securirty and Communication Networks, 9(17), pp.4089~4102 (2016)
- A. Peng, "Median filtering forensics using multiple models in residual domain", IEEE Access, 7, pp.28525~28538 (2019)
- H. Gao, "Detection of median filtering based on ARMA model and pixel-pair histogram feature of difference image", Multimedia Tools and Applications, pp.12551~12567 (2020)
- H. Gao, "Robust detection of median filtering based on data-pair histogram feature and local configuration pattern", Journal of Information Security and Applications, 53, pp.102506~ (2020)
- D. Wang, "Filtered image forensics based on frequency domain features", International Conference on Communication Technology, pp.1208~1212 (2019)
- A. Peng, "A universal image forensics of smoothing filtering", International Journal of Digital Crime and Forensics, 11(1), pp.18~28 (2019)
- A. Gupta, "A simplistic global median filtering forensics based on frequency domain analysis of image residuals", ACM Transactions on Multimedia Computing, Communications, and Applications, 15(3) (2019)
- T. Ahonen, "Face recognition with local binary patterns", European Conference on Computer Vision, pp.469~481 (2004)
- G. Zhao, "Rotation-invariant image and video description with local binary pattern features", IEEE Transactions on Image Processing, 21(4) (2012)
- R. Nosaka, "Rotation invariant co-occurrence among adjacent LBPs", Asian Conference on Computer Vision, pp.15~25 (2012)
- R. Nosaka, "HEp-2 cell classification using rotation invariant co-occurrence among local binary patterns", Pattern Recognition, 47(7), pp.2428~2436 (2014)
- G. Schaefer, "UCID - an uncompressed colour image database", Proceedings of the SPIE, 5307, pp.472~480 (2003)