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RETRACTED ARTICLE: LPG: a novel approach for medical forgery detection in image transmission

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This article was retracted on 13 June 2022

This article has been updated

Abstract

Medical image transmission using IoT has become the hot field in the today’s world of research, but the attacks or manipulating the images, has become the real threat to the medical field. Physicians diagnose always depends on the digital image(s). Small change in the medical image may threaten patient’s life. Early detection of forgery may help a patient’s life from danger. Hence the most intelligent algorithm developing is required for the above mentioned attacks. To meet the above criteria, most intelligent LPG algorithm has been proposed. LPG algorithm has been integrated with the cognitive extreme learning machines for detection. The proposed algorithm has been evaluated with the mammograms breast cancer images and accuracy detection is found to be more accuracy based on activation function compared with the other existing recent papers.

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Abbreviations

LPG:

Local patterns of gray level

ELM:

Extreme learning machine

LBP:

Local binary pattern

GLCM:

Gray level co variance matrix

SIFT:

Scale-invariant feature transform

SURF:

Speeded-up robust features

FLBA:

Fuzzy logic bat algorithm

MOBA:

Multi-objective bat algorithm

KMBA:

K-means bat algorithm

BBA:

Binary bat algorithm

CBA:

Chaotic bat algorithm

IBA:

Improved bat algorithm

DLBA:

Differential operator and Lévy flight bat algorithm

MIAS:

Mammographic Image Analysis Society

PCA:

Principal component analysis

ZM:

Zernike moments

GIMP:

GNU Image Manipulation Program

HOG:

Histograms of gradients

EM algorithm:

Expectation–maximization algorithm

AWGN:

Additive White Gaussian noise

GB:

Gaussian blurring

GN:

Gaussian noise

SVM:

Support vector machine

ILBP:

Improved LBP

MBP:

Median binary patterns

LTP:

Local ternary patterns

ILTP:

Improved LTP

RLBP:

Robust LBP

SLBP:

Significant LBP

{CE, CO, H}:

{Contrast, energy, correlation, homogeneity}

MATLAB:

Matrix laboratory

References

  • Albregsten F (1995) Statistical texture measures computed from gray level co-occurrence matrices. Technical note. Department of Informatics, University of Oslo, Oslo, Oslo

    Google Scholar 

  • Arun Anoop M, Poonkuntran S (2018) DFE approach for CMFD on digital images—a review and performance evaluation. Int J Comput Technol 5(11):148–159

    Google Scholar 

  • Arun Anoop M, Poonkuntran S (2019) Lora approach for image forgery detection and localization in digital images. CnR’s Int J Soc Sci Res 4(3):1–14

    Google Scholar 

  • Azemin MZC, Tamrin MIM, Hilmi MR, Kamal KM (2015) GLCM texture analysis on different color space for pterygium grading. ARPN J Eng Appl Sci 10(15):6410–6413

    Google Scholar 

  • Calberson FL, Hommez GM, De Moor RJ (2008) Fraudulent use of digital radiography: methods to detect and protect digital radiographs. Am Assoc Endod JOE 34(5):5. https://doi.org/10.1016/j.joen.2008.01.019

    Article  Google Scholar 

  • Díaz-Flores-García V, Labajo-Gonzalez E, Santiago-Saez A, Perea-Perez B (2017) Detecting the manipulation of digital clinical records in dental practice. Coll Radiogr. https://doi.org/10.1016/j.radi.2017.05.003

    Article  Google Scholar 

  • Feng G, Huang G-B, Lin Q, Gay R (2009) Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Trans Neural Netw 20(8):1352–1357

    Article  Google Scholar 

  • Ghoneim A, Muhammad G, Amin SU, Gupta B (2018) Medical image forgery detection for smart healthcare. Adv Next Gener Netw Technol Smart Healthc IEEE Commun Mag. https://doi.org/10.1109/MCOM.2018.1700817

    Article  Google Scholar 

  • Girisha AB, Chandrashekhar MC, Kurian MZ (2013) Texture feature extraction of video frames using GLCM. Int J Eng Trends Technol 4(6):2718–2721

    Google Scholar 

  • Guo Y, Cao X, Zhang W, Wang R (2018) Fake colorized image detection. IEEE Trans Inf Forensics Secur 13(8):1932–1944

    Article  Google Scholar 

  • Heikkilä M, Pietikäinen M (2006) A texture-based method for modeling the background and detecting moving objects. IEEE Trans Pattern Anal Mach Intell 28(4):657–662

    Article  Google Scholar 

  • Kylberg G, Sintorn IM (2013) Evaluation of noise robustness for local binary pattern descriptors in texture classification. EURASIP J Image Video Process 1:17

    Article  Google Scholar 

  • Kylberg G, Sintorn I-M (2016) On the influence of interpolation method on rotation invariance in texture recognition. EURASIP J Image Video Process 2016:17

    Article  Google Scholar 

  • Lee JC, Chang CP, Chen WK (2015) Detection of copy–move image forgery using histogram of orientated gradients. Inf Sci. https://doi.org/10.1016/j.ins.2015.03.009

    Article  Google Scholar 

  • Li W, Yuan W (2018) Multiple palm features extraction method based on vein and palmprint. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-018-0699-1

    Article  Google Scholar 

  • Mehrish A, Subramanyam AV, Emmanuel S (2019) Joint spatial and discrete cosine transform domain-based counter forensics for adaptive contrast enhancement. IEEE Access 7:27183–27195

    Article  Google Scholar 

  • Mondal S, Mukherjee J (2015) Image similarity measurement using region props, color and texture: an approach. Int J Comput Appl 121(22):23–26

    Google Scholar 

  • Nanni L, Brahnam S, Lumini A (2010) A local approach based on a local binary patterns variant texture descriptor for classifying pain states. Expert Syst Appl 37(12):7888–7894

    Article  Google Scholar 

  • Nasiri M, Behrad A (2019) Using expectation-maximization for exposing image forgeries by revealing inconsistencies in shadow geometry. J Vis Commun Image R 58(2019):323–333

    Article  Google Scholar 

  • Prabu S, Balamurugan V, Vengatesan K (2019) Design of cognitive image filters for suppression of noise level in medical images. Measurement 141:296–301

    Article  Google Scholar 

  • Prabu S, Lakshmanan M, Mohammed VN (2019) A multimodal authentication for biometric recognition system using intelligent hybrid fusion techniques. J Med Syst 43:249

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Ravichandran D, Praveenkumar P, Rayappan JBB, Amirtharajan R (2017) DNA Chaos blend to secure medical privacy. IEEE Trans Nanobiosci 16(8):850–859

    Article  Google Scholar 

  • Roy A, Konda A, Chakraborty RS (2017) Copy move forgery detection with similar but genuine objects. ICIP IEEE. https://doi.org/10.1109/ICIP.2017.8297050

    Article  Google Scholar 

  • Soni B, Das PK, Thounaojam DM (2017) Copy-move tampering detection based on local binary pattern histogram Fourier feature. In: ICCCT-2017: proceedings of the 7th international conference on computer and communication technology, November 2017, pp 78–83

  • Suckling J, Parker J, Dance D, Astley S, Hutt I, Boggis C, Ricketts I, Stamatakis E, Cerneaz N, Kok S-L, Taylor P, Betal D, Savage J (1994) Mammographic image analysis society digital mammogram database. Elsevier, Amsterdam, pp 375–378

    Google Scholar 

  • Tan X, Triggs B (2007) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):635–1650

    MathSciNet  MATH  Google Scholar 

  • Tralic D, Grgic S, Zovko-Cihlar B (2014a) Video frame copy-move forgery detection based on Cellular Automata and Local Binary Patterns. In: 2014 X international symposium on telecommunications (BIHTEL), Sarajevo, pp 1–4

  • Tralic D, Rosin PL, Sun X, Grgic S (2014b) Copy-move forgery detection using cellular automata. In: Rosin P et al (eds) Cellular automata in image processing and geometry, 105 emergence, complexity and computation, vol 10. Springer International Publishing, Amsterdam. https://doi.org/10.1007/978-3-319-06431-4_6

  • Tuncer T, Dogan S (2019) Pyramid and multi kernel based local binary pattern for texture recognition. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01306-1

    Article  Google Scholar 

  • Wang Y, Tian L, Li C (2017) LBP-SVD based copy move forgery detection algorithm. IEEE Int Symp Multimedia. https://doi.org/10.1109/ISM.2017.108

    Article  Google Scholar 

  • Warif NBA, Idris MYI, Wahab AWA, Salleh R, Ismail A (2018) CMF-Items: an automatic threshold selection for detection of copy-move forgery. FSI. https://doi.org/10.1016/j.forsciint.2018.12.004

    Article  Google Scholar 

  • Wu Y, Abd-Almageed W, Natarajan P (2018) BusterNet: detecting copy-move image forgery with source/target localization. In: European conference on computer vision (ECCV)

  • Yan Y, Ren W, Cao X (2019) Recolored image detection via a deep discriminative model. IEEE Trans Inf Forensics Security 14(1):5–17

    Article  Google Scholar 

  • Yang F, Li J, Lu W, Weng J (2017) Copy-move forgery detection based on hybrid features. Eng Appl Artif Intell 59(2017):73–83. https://doi.org/10.1016/j.engappai.2016.12.022

    Article  Google Scholar 

  • Yaseen ZM, Deo RC, Hilal A, Abd AM, Bueno LC, Salcedo-Sanz S, Nehdi ML (2017) Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Adv Eng Softw. https://doi.org/10.1016/j.advengsoft.2017.09.004

    Article  Google Scholar 

  • Zhuo L, Tan S, Zeng J, Lit B (2018) Fake colorized image detection with channel-wise convolution based deep-learning framework. In: 2018 Asia-Pacific signal and information processing association annual summit and conference (APSIPA ASC), Honolulu, HI, USA, pp 733–736

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Velammal College of Engineering and Technology, Viraganoor, Madurai, Tamil Nadu, India

    M. Arun Anoop & S. Poonkuntran

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  1. M. Arun Anoop
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  2. S. Poonkuntran
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Correspondence to M. Arun Anoop.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04161-9

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Arun Anoop, M., Poonkuntran, S. RETRACTED ARTICLE: LPG: a novel approach for medical forgery detection in image transmission. J Ambient Intell Human Comput 12, 4925–4941 (2021). https://doi.org/10.1007/s12652-020-01932-0

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  • DOI: https://doi.org/10.1007/s12652-020-01932-0

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