109
Views
1
CrossRef citations to date
0
Altmetric
Articles

Copy-move forgery detection of medical images using golden ball optimization

, &
Pages 729-737 | Received 11 Jul 2020, Accepted 21 Mar 2021, Published online: 04 Apr 2021

References

  • Solanas A, Patsakis C, Conti M, et al. Smart health: A context-aware health paradigm within smart cities. IEEE Commun Mag. 2014;52:74–81.
  • Ghoneim A, Muhammad G, Amin S, et al. Medical image forgery detection for smart healthcare. IEEE Commun Mag. 2018;56:33–37.
  • Transpire Online. The feedback artificial tree algorithm (FAT): great potential to solve wide range of practical optimization problems, transpire Online 2019. [cited 2019 Sep 15]. Available from: https://transpireonline.blog/2020/05/29/the-feedback-artificial-tree-algorithm-fat-great-potential-to-solve-wide-range-of-practical-optimization-problems/.
  • Mythili S, Thiyagarajah K, Rajesh P, et al. Ideal position and size selection of unified power flow controllers (UPFCs) to upgrade the dynamic stability of systems: An antlion optimiser and invasive weed optimisation algorithm. HKIE Trans. 2020;27:25–37.
  • Kok K, Rajendran P. Validation of Harris fetector and eigen features detector. IOP Conf Ser: Mater Sci Eng. 2018;370:012013.
  • Osaba E, Diaz F, Onieva E. Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts. Appl Intell. 2014;41(1):145–166.
  • Wu J, Chang R, Chen C, et al. Tamper detection and recovery for medical images using near-lossless Information hiding technique. J Digit Imaging. 2008;21:59–76.
  • Lim S, Moon H, Chae S, et al. Dual watermarking method for integrity of medical images. 2008 second International Conference on Future Generation Communication and Networking; 2008.
  • Fotopoulos V, Stavrinou M, Skodras A. Medical image authentication and self-correction through an adaptive reversible watermarking technique. 2008 8th IEEE International Conference on BioInformatics and Bio engineering; 2008.
  • Coatrieux G, Le Guillou C, Cauvin J, et al. Reversible watermarking for knowledge digest embedding and reliability control in medical images. IEEE Trans Inf Technol Biomed. 2009;13:158–165.
  • Siau-Chuin L, Siau-Way L, Jasni MZ. Reversible medical image watermarking for tamper detection And recovery with run length encoding compression. World Acad Sci Eng Technol. 2010;4:674–678.
  • Olanrewaju RF, Othman O, Khalifa A-HH, et al. Forgery detection in medical images using complex valued neural network (CVNN). Aust J Basic Appl Sci. 2011;5:1251–1264.
  • Singh A, Dave M, Mohan A. Robust and secure multiple watermarking in wavelet domain. J Med Imaging Health Inform. 2015;5:406–414.
  • Eswaraiah R, Sreenivasa Reddy E. Robust medical image watermarking technique for accurate detection of tampers inside region of interest and recovering original region of interest. IET Image Proc. 2015;9:615–625.
  • Gao G, Wan X, Yao S, et al. Reversible data hiding with contrast enhancement and tamper localization for medical images. Inf Sci. 2017;385–386:250–265.
  • Ranjani JJ, Babu M. Medical image reliability verification using hash signatures and sequential square encoding. J. Intell. Syst. 2018;27(1):19–30.
  • Goléa N, Melkemi K. ROI-based fragile watermarking for medical image tamper detection. Int J High Perform Comput Networking. 2019;13(2):199–210.
  • Khan M, Khan H, Yousaf A, et al. Modern trends in hyperspectral image analysis: A review. IEEE Access. 2018;6:14118–14129.
  • Chen L, Gao S, Cao X. Research on real-time outlier detection over big data streams. Int J Comput Appl. 2017;42:93–101.
  • Khan M, Yousaf A, Khurshid K, et al. Automated forgery detection in multispectral document images using fuzzy clustering. 2018 13th IAPR International Workshop on Document Analysis systems (DAS); 2018.
  • Charan S, Khan M, Khurshid K. Breast cancer detection in mammograms using convolutional neural network). 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET); 2018.
  • Brunese L, Mercaldo F, Reginelli A, et al. Radiomic features for medical images tamper detection by equivalence checking. Procedia Comput Sci. 2019;159:1795–1802.
  • Patel P DK. Smart healthcare forgery detection using deep learning. Int J Adv Res Innov Ideas Educ. 2019;5(3):1670–1674.
  • Khan M, Khurshid K, Shafait F. A spatio-spectral hybrid convolutional architecture for hyperspectral document authentication. 2019 International Conference on Document Analysis and Recognition (ICDAR); 2019.
  • Maier A, Syben C, Lasser T, et al. A gentle introduction to deep learning in medical image processing. Z Med Phys. 2019;29:86–101.
  • Meena K, Tyagi V. A hybrid copy-move image forgery detection technique based on Fourier-Mellin and scale invariant feature transforms. Multimed Tools Appl. 2020;79:8197–8212.
  • Ahmad H, Khan M, Yousaf A, et al. Deep learning: a breakthrough in medical imaging. Curr. Med. Imag. Formerly Curr. Med. Imag. Rev. 2020;16:946–956.
  • Tian X, Zhou G, Xu M. Image copy-move forgery detection algorithm based on ORB and novel similarity metric. IET Image Proc. 2020;14:2092–2100.
  • Priyanka SG, Singh K. An improved block based copy-move forgery detection technique. Multimed Tools Appl. 2020;79(19–20):13011–13035.
  • Park J, Kang T, Moon Y, et al. Copy-move forgery detection using scale invariant feature and reduced local binary pattern histogram. Symmetry (Basel). 2020;12(4):492.
  • Bilal M, Habib H, Mehmood Z, et al. A robust technique for copy-move forgery detection from small and extremely smooth tampered regions based on the DHE-SURF features and mDBSCAN clustering. Aust J Forensic Sci. 2020: 1–24.
  • Suruliandi A, Kavitha J, Nagarajan D. An empirical evaluation of recent texture features for the classification of natural images. Int J Comput Appl. 2020;42:164–173.
  • Jallouli M, Lajmi S, Amous I. When contextual information meets recommender systems: extended SVD++ models. Int J Comput Appl. 2020:1–8. doi.org/10.1080/1206212X.2020.1752971.
  • Adinugroho S, Wihandika R, Adikara P. Newsgroup topic extraction using term-cluster weighting and pillar K-means clustering. Int J Comput Appl. 2020: 1–8. doi.org/10.1080/1206212X.2020.1757246.
  • Veerashetty S, Patil N. Manhattan distance-based histogram of oriented gradients for content-based medical image retrieval. Int J Comput Appl. 2019: 1–7. doi.org/10.1080/1206212X.2019.1653011.
  • Ren D. Research and analysis on precise matching method for multi-feature of fuzzy digital image. Int J Comput Appl. 2020;42:141–149.
  • https://www.ctisus.com/teachingfiles/chest/285194, accessed on Oct 2020.
  • Hashmi M, Hambarde A, Keskar A. Copy move forgery detection using DWT and SIFT features. 2013 13th International Conference on Intellient Systems Design and Applications; 2013.
  • Yu L, Han Q, Niu X. Copy-Rotation-Move forgery detection using the MROGH descriptor. 2014 IEEE International Conference on Cloud Engineering; 2014.
  • Yohannan R, Manuel M. Detection of copy-move forgery based on Gabor filter. 2016 IEEE International Conference on Engineering and Technology (ICETECH); 2016.
  • Mehak, Tarun G. Improve copy move forgery image classification by optimization technique. Int. J. Adv. Eng. Res. 2017;13(5):19–29.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.