Total views : 285
Image Forgery Detection using SIFT and PCA Classifiers for Panchromatic Images
Objectives: The image forgery detection is the technique in which pixels are marked in the image, which are not similar to other pixels of the images. The Principal Component Analysis (PCA) is the classification of neural networks which will analyze each pixel of the image and classify pixels according to pixel type. Method: The PCA algorithm takes training and trained dataset as input and drive new values according to input image. In the paper improvement in the PCA algorithm with usage of Scale-Invariant-Feature-Transform algorithm (SIFT Algorithm), is proposed for image-forgery. The SIFT algorithm is the algorithm which analyze each pixel of the image and define type of pixels in the image. The output of the SIFT algorithm is given as input to PCA algorithm for data classification. The PCA algorithm will classify the data according to SIFT algorithm output. Findings: The results demonstrate that the proposed algorithm executes well in terms of “Peak Signal-to-Noise Ratio” (PSNR), “Mean-Square-Error” (MSE), fault detection rate and accuracy value.
Forgery Detection, MSE, PCA, PSNR, SIFT.
- Murali S, Govindraj B, Chittapur C, Prabhakara HS, Basavaraj S, Anami A. Comparison and analysis of photo image forgery detection techniques. IJCSA. 2012; 2(6):1–1.
- Gomase MPG, Wankhade MNR. Advanced digital image forgery detection- A review. IOSR-JCE. 2014; 4(3):80–3.
- Rohini A, Maind M, Khade A, Chitre DK. Image copy move forgery detection using block representing method. IJSCE. 2014; 4(2):180–9.
- Suresh G, Rao CS. RST invariant image forgery detection. Indian Journal of Science and Technology. 2016 Jun; 9(21):1–8.
- Elwin JGR, Kousalya G. Image forgery detection using multidimensional spectral hashing based polar cosine transform. Indian Journal of Science and Technology. 2015 May; 8(S9):1–12.
- Kalaivani R, Sudhagar K, Lakshmi P. Neural network based vibration control for vehicle active suspension system. Indian Journal of Science and Technology. 2016 Jan; 9(1):1–8.
- Anitha K, Leveenbose P. Edge detection based salient region detection for accurate image forgery detection. IEEE. 2014; 12(10):1–4.
- Moreno R, Puig D, Julia C, Garcia MA. A new methodology for evaluation of edge detectors. Proceedings of the16th IEEE International Conference on Image Processing (ICIP); Cairo. 2009. p. 2157–60.
- Swaminathan A, Wu M, Liu KJR. Digital image forensics via intrinsic fingerprints. IEEE Transactions on Information Forensics and Security. 2008; 3(1):101–17.
- Mikolajczyk K, Schmid C. A performance evaluation of local descriptors, pattern analysis and machine intelligence. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2005; 27(10):1615–30.
- Gupta G. Improved median filter and comparison of mean, median and improved median filter. IJSCE. 2011; 1(5):819–27.
- Maeno K, Nagahara H, Shimada A, Taniguchi RI. Light field distortion feature for transparent object recognition. IEEE explore Computer Vision Foundation. 2013; 6(1):2786–93.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.