Abstract
Digital images face widespread tampering through a copy-move attack, wherein objects are added or removed from an image to create the copy-move forged image. This paper proposes a robust Copy Move Forgery Detection (CMFD) scheme by integrating both block-based and keypoint-based CMFD techniques. The input image is first segmented into non-overlapping blocks using a modified FCM clustering algorithm based on superpixels. The neighboring and similar superpixels influences are encompassed into FCM, and Emperor Penguin Optimization (EPO) is used to optimize the influential degree, which increases the segmentation performance. Gabor filter is applied to extract the features from each block. The Block features are then matched with each other to show the suspected forgery regions. The experimental results illustrate that the proposed technique attains better performance in terms of precision, recall, F1-score, and E-measure. It accomplishes excellent forgery detection outcomes compared with other existing CMFD processes, even under different perplexing circumstances.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alkawaz MH, Sulong G, Saba T, Rehman A (2018) Detection of copy-move image forgery based on discrete cosine transform. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2663-3
Amerini I, Ballan L, Caldelli R et al (2011) A SIFT-based forensic method for copy-move attack detection and transformation recovery. IEEE Trans Inf Forensics Secur 6:1099–1110. https://doi.org/10.1109/TIFS.2011.2129512
Amerini I, Ballan L, Caldelli R et al (2013) Copy-move forgery detection and localization by means of robust clustering with J-Linkage. Signal Process Image Commun. https://doi.org/10.1016/j.image.2013.03.006
Askari S (2020) Fuzzy C-means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: review and development. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.113856
Bayram S, Sencar HT, Memon N (2009) An efficient and robust method for detecting copy-move forgery. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings.
Bi XL, Pun CM, Yuan XC (2018) Multi-scale feature extraction and adaptive matching for copy-move forgery detection. Multimed Tools Appl. https://doi.org/10.1007/s11042-016-4276-3
Cao Y, Wu Y, Fu L et al (2019) Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics. Energy Rep. https://doi.org/10.1016/j.egyr.2019.10.029
Dhiman G, Kaur A (2018) Optimizing the design of airfoil and optical buffer problems using spotted hyena optimizer. Designs. https://doi.org/10.3390/designs2030028
Dhiman G, Kaur A (2019) STOA: A bio-inspired based optimization algorithm for industrial engineering problems. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2019.03.021
Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw. https://doi.org/10.1016/j.advengsoft.2017.05.014
Dhiman G, Kumar V (2018) Emperor penguin optimizer: a bio-inspired algorithm for engineering problems. Knowl-Based Syst. https://doi.org/10.1016/j.knosys.2018.06.001
Di Martino F, Senatore S, Sessa S (2019) A lightweight clustering–based approach to discover different emotional shades from social message streams. Int J Intell Syst. https://doi.org/10.1002/int.22105
Dixit R, Naskar R, Mishra S (2017) Blur-invariant copy-move forgery detection technique with improved detection accuracy utilising SWT-SVD. IET Image Process. https://doi.org/10.1049/iet-ipr.2016.0537
Fan DP, Gong C, Cao Y, Ren B, Cheng MM, Borji A (2018) Enhanced-alignment measure for binary foreground map evaluation. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp 698–704
Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM. https://doi.org/10.1145/358669.358692
Fridrich J, Soukal D, Lukáš J (2003) Detection of copy-move forgery in digital images. Int J 3:4
Han J, Ma KK (2007) Rotation-invariant and scale-invariant Gabor features for texture image retrieval. Image Vis Comput. https://doi.org/10.1016/j.imavis.2006.12.015
Hossein-Nejad Z, Nasri M (2017) An adaptive image registration method based on SIFT features and RANSAC transform. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2016.11.034
Jia H, Sun K, Song W et al (2019) Multi-strategy emperor penguin optimizer for RGB histogram-based color satellite image segmentation using masi entropy. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2942064
Levinshtein A, Stere A, Kutulakos KN, et al (2009) TurboPixels: Fast superpixels using geometric flows. In: IEEE Transactions on Pattern Analysis and Machine Intelligence.
Li M, Hu S, Xia J et al (2020) Dissolved oxygen model predictive control for activated sludge process model based on the Fuzzy C-means cluster algorithm. Int J Control Autom Syst. https://doi.org/10.1007/s12555-019-0438-1
Liu Z, Meur L, Luo S (2013) Superpixel-based saliency detection. In: International Workshop on Image Analysis for Multimedia Interactive Services.
Popescu AC, Farid H (2004) Exposing Digital Forgeries by Detecting Duplicated Image Regions. Tech Report, TR2004–515, Dep Comput Sci Dartmouth Coll Hanover, New Hampsh,pp 1–11
Pun CM, Yuan XC, Bi XL (2015) Image forgery detection using adaptive oversegmentation and feature point matching. IEEE Trans Inf Forensics Secur. https://doi.org/10.1109/TIFS.2015.2423261
Qureshi MA, Deriche M (2015) A bibliography of pixel-based blind image forgery detection techniques. Signal Process Image Commun. https://doi.org/10.1016/j.image.2015.08.008
Raju PM, Nair MS (2018) Copy-move forgery detection using binary discriminant features. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2018.11.004
Ryu SJ, Kirchner M, Lee MJ, Lee HK (2013) Rotation invariant localization of duplicated image regions based on zernike moments. IEEE Trans Inf Forensics Secur. https://doi.org/10.1109/TIFS.2013.2272377
Shivakumar BL, Baboo S (2011) Detection of Region Duplication Forgery in Digital Images Using SURF. Int J Comput Sci Issues 8:199–205
Tian X, Jiao L, Yi L et al (2015) The image segmentation based on optimized spatial feature of superpixel. J Vis Commun Image Represent. https://doi.org/10.1016/j.jvcir.2014.11.005
Wang XY, Liu YN, Xu H et al (2018) Robust copy–move forgery detection using quaternion exponent moments. Pattern Anal Appl. https://doi.org/10.1007/s10044-016-0588-1
Yang F, Li J, Lu W, Weng J (2017) Copy-move forgery detection based on hybrid features. Eng Appl Artif Intell 59:73–83. https://doi.org/10.1016/j.engappai.2016.12.022
Yang B, Sun X, Guo H et al (2018) A copy-move forgery detection method based on CMFD-SIFT. Multimed Tools Appl. https://doi.org/10.1007/s11042-016-4289-y
Yohannan RP, Manuel M (2016) Detection of copy-move forgery based on Gabor filter. In: Proceedings of 2nd IEEE International Conference on Engineering and Technology, ICETECH 2016.
Zheng J, Liu Y, Ren J et al (2016) Fusion of block and keypoints based approaches for effective copy-move image forgery detection. Multidimens Syst Signal Process 27:989–1005. https://doi.org/10.1007/s11045-016-0416-1
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Agarwal, R., Verma, O.P. Robust copy-move forgery detection using modified superpixel based FCM clustering with emperor penguin optimization and block feature matching. Evolving Systems 13, 27–41 (2022). https://doi.org/10.1007/s12530-021-09367-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12530-021-09367-4