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A novel image tamper detection approach by blending forensic tools and optimized CNN: Sealion customized firefly algorithm

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Abstract

In recent days, the digital images are manipulated more professionally and easily via common image processing tools. This has been highly practiced in diverse applications including the surveillance systems, where the tamper detection with higher reliability is essential. A novel image tamper detection framework is designed with two major phases: fused feature extraction framework and tamper detection. The collected data are subjected to the fused feature extraction framework, where the features like adaptive speeded up robust features (SURF), Discrete Wavelet Transform (DWT) based Patched Local Vector Pattern (LVP) features, Proposed Principal Component Analysis (PCA) based Histogram of Oriented Gradients (HoG) feature and Mode Based First Digit Feature (MBFDF) are extracted. Subsequently, the extracted features are fed as the input to Optimized Convolutional Neural Network (CNN), which results in the type of tampering in the image: copy-move, splicing, noise inconsistency and double compression. To make the detection more accurate, the weights of CNN are fine-tuned by a new hybrid optimization algorithm referred as Sealion Customized Firefly algorithm (SCFF). The proposed hybrid optimization algorithm is the amalgamation of the standard Sea Lion Optimization Algorithm (SLnO) and Firefly Algorithm (FF). Finally, a comparative evaluation is made between the proposed and existing works in terms of certain performance measures as well.

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Abbreviations

CNN:

Convolutional Neural Network

CS:

Compressive Sensing

FNR:

False Negative Rate

DFT:

Discrete Fourier Transform

HoG:

Histogram Of Oriented Gradients

FDR:

False Discovery Rate

FF:

Firefly Algorithm

FOR:

False Omission Rate

FPR:

False Positive Rate

DCT:

Discrete CosineTransform

IWT:

Integer Wavelet Transform

LVP:

Local Vector Pattern

MBFDF:

Mode Based First Digit Feature

MCC:

Mathews Correlation Coefficient

MK:

Markedness

NPV:

Negative Predictive Value

NSCT:

Non-Subsampled Contourlet Transform

SCFF:

Sealion With Customized Firefly

SLnO:

Sealion Optimization Algorithm

SURF:

Speeded Up Robust Features

P-Test:

Probability -Test

TRLG:

Tamper Detection And Recovery Based On Lifting Wavelet Transform And Genetic Algorithm

DWT:

Discrete Wavelet Domain

References

  1. Abdelhakim A, Saleh HI, Abdelhakim M (2019) Fragile watermarking for image tamper detection and localization with effective recovery capability using K-means clustering. Multimed Tools Appl. 78(22):32523–32563

    Article  Google Scholar 

  2. Ahmad M, Khursheed F (2021) Digital image detection approaches: a review applications of artificial intelligence in engineering. Springer, Singapore, pp 863–882

    Book  Google Scholar 

  3. Al-Wesabi FN, Mahmood K, Nemri N (2020) A zero watermarking approach for content authentication and tampering detection of Arabic text based on fourth level order and word mechanism of Markov model. J Inform Secur Appl 52:102473

    Google Scholar 

  4. Amerini I, Ballan L, Caldelli R, Bimbo AD, Serra G (2011) A sift-based forensic method for copy–move attack detection and transformation recovery. IEEE T Inf Foren Sec 6(3):1099–1110

    Article  Google Scholar 

  5. Aramini B, Geraghty P, Lederer DJ, Costa J, Angelo SLD, Floros J, D’Ovidio F (2019) Surfactant protein A and D polymorphisms and methylprednisolone pharmacogenetics in donor lungs. J Thorac Cardiov Sur 157(5):2109–2117

  6. Arul VH, Sivakumar VG, Marimuthu R, Chakraborty B (2019) An Approach for speech enhancement using deep convolutional neural network. Multimed Res 2(1):37–44

    Google Scholar 

  7. Barani MJ, Valandar MY, Ayubi P (2019) A new digital image tamper detection algorithm based on integer wavelet transform and secured by encrypted authentication sequence with 3D quantum map. Optik 187:205–222

    Article  Google Scholar 

  8. Belferdi W, Behloul A, Noui L (2018) A Bayer pattern-based fragile watermarking schemefor color image tamper detection and restoration. Multidimension Syst Signal Process 30(3):1093–1112

    Article  Google Scholar 

  9. Boothalingam R (2018) Optimization using lion algorithm: a biological inspiration from lion’s social behavior. Evol Intel 11(1–2):31–52

    Article  Google Scholar 

  10. Brajula W, Prasad MB (2018) ODFF opposition and dimension based firefly for optimal reactive power dispatch. J Comput Mech, Power Syst Control 1(1):1–10

    Article  Google Scholar 

  11. Brunese L, Mercaldo F, Reginelli A, Santone A (2019) Radiomic Features for Medical Images Tamper Detection by Equivalence Checking. Procedia Comput Sci 159:1795–1802

    Article  Google Scholar 

  12. Cattaneo G, Roscigno G, Petrillo UF (2018) Improving the experimental analysis of tampered image detection algorithms for biometric systems. Pattern Recogn Lett 113:93–101

    Article  Google Scholar 

  13. Chen B, Tan W, Coatrieux G, Zheng Y, Shi YQ (2020) A serial image copy-move forgery localization scheme with source/target distinguishment. IEEE T Multimedia. https://doi.org/10.1109/TMM.2020.3026868

    Article  Google Scholar 

  14. Fan KC, Hung T-Y (2014) A novel local pattern descriptor-local vector pattern in high-order derivative space for face recognition. Ieee T Image Process 23:2877–2891

    Article  MathSciNet  Google Scholar 

  15. Fister I, Fisterjr I, Sheyang X, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34–46

    Article  Google Scholar 

  16. Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Cai J, Chen T (2018) Recent advances in convolutional neural networks. Pattern Recogn 77:354–377

    Article  Google Scholar 

  17. Haghighi BB, Taherinia AH, Mohajerzadeh AH (2019) TRLG: Fragile blind quad watermarking for image tamper detection and recovery by providing compact digests with optimized quality using LWT and GA. Inform Sci 486:204–230

    Article  Google Scholar 

  18. Hsu C-S, Tu S-F (2016) Image tamper detection and recovery using adaptive embedding rules. Meas 88:287–296

    Article  Google Scholar 

  19. Kaur M, Gupta S (2017) A Fusion Framework based on fuzzy integrals for passive-blind image tamper detection. Cluster Comput 16

  20. Lugaresi M, Aramini B, Daddi N, Baldi F, Mattioli S (2015) Effectiveness of antireflux surgery for the cure of chronic cough associated with gastroesophageal reflux disease. World J Surg 39(1):208–215

    Article  Google Scholar 

  21. Lv L, Zhao J, Wang J, Fan T (2019) Multi-Objective firefly algorithm based on compensation factor and elite learning. Future Gener Comp Sy 91:37–47

    Article  Google Scholar 

  22. Manu VT, Mehtre BM (2019) Tamper detection of social media images using quality artifacts and texture features. Forensic Sci Int 295:100–112

    Article  Google Scholar 

  23. Martino FD, Sessa S (2018) Fragile watermarking tamper detection via bilinear fuzzy relation equations. J Amb Intel Hum Comp. 10(5):2041–2061

    Article  Google Scholar 

  24. Masadeh R, Mahafzah BA, Sharieh A (2019) Sea lion optimization algorithm. Inter J Adv Comput Sci Appl (IJACSA) 10(5)

  25. Molina-Garcia J, Garcia-Salgado BP (2020) Volodymyr Ponomaryov, Rogelio Reyes-Reyes, Clara Cruz-Ramos.An effective fragile watermarking scheme for color image tampering detection and self-recovery. Signal Process: Image Commun 81:115725

  26. Ng T-T, Chang S-F (2004a) A model for image splicing. In 2004 InterConf Image Process 2:1169–1172

  27. Ng TT, Chang SF (2004b) Blind detection of digital photomontage using higher

  28. Ng T-T, Chang S-F, Sun Q (2004) Blind detection of photomontage using higher order statistics. In 2004 IEEE inter symposium on circuits and systems (IEEE Cat. No. 04CH37512), 5: V-V. IEEE

  29. Rajakumar BR (2013) Impact of static and adaptive mutation techniques on genetic algorithm. Inter J Hybrid Intell Syst 10(1):11–22. https://doi.org/10.3233/HIS-120161

    Article  Google Scholar 

  30. Rajakumar BR (2014) Lion algorithm for standard and large scale bilinear system identification: A global optimization based on Lion’s social behavior. IEEE C Evol Computat 2014:2116–2123. https://doi.org/10.1109/CEC.2014.6900561

  31. Rajakumar BR, George A (2012) A new adaptive mutation technique for genetic algorithm. Proceed IEEE Inter Conf Comput Intel Comput Res (ICCIC) 1–7:18–20. https://doi.org/10.1109/ICCIC.2012.6510293

    Article  Google Scholar 

  32. Sharma S, Ghanekar U (2018) A hybrid technique to discriminate natural images, computer generated graphics images, spliced, copy move tampered images and authentic images by using features and ELM classifier. Optik 172:470–483

    Article  Google Scholar 

  33. Singh D, Singh SK (2016) Effective self-embedding watermarking scheme for image tampered detection and localization with recovery capability. J Vis Commun Image R 38:775–789

    Article  Google Scholar 

  34. Sivasubramanian N, Konganathan G (2020) A novel semi fragile watermarking technique for tamper detection and recovery using IWT and DCT. Comp

  35. Swamy SM, Rajakumar BR, Valarmathi IR (2013) Design of hybrid wind and photovoltaic power system using opposition-based genetic algorithm with cauchy mutation. IET Chennai Fourth Inter Conf Sustain Energ Intell Syst (SEISCON 2013), Chennai, India. https://doi.org/10.1049/ic.2013.0361

  36. Swaraja K, Meenakshi K, Kora P (2020) An optimized blind dual medical image watermarking framework for tamper localization and content authentication in secured telemedicine. Biomed Signal Proces 55:101665

    Article  Google Scholar 

  37. Thanki R, Borra S (2018) Fragile watermarking for copyright authentication and tamper detection of medical images using compressive sensing (CS) based encryption and contourlet domain processing. Multimed Tools Appl. 78(10):13905–13924

    Article  Google Scholar 

  38. Tiwari A, Sharma M, Tamrakar RK (2017) Watermarking based image authentication and tamper detection algorithm using vector quantization approach. AEU - Inter J Elect Commun 78:114–123

    Article  Google Scholar 

  39. Trivedy S, Pal AK (2017) A logistic map-based fragile watermarking scheme of digital images with tamper detection. Iran J Sci Techy T Electr Eng. 41(2):103–113

    Article  Google Scholar 

  40. Vega EAA, Fernández EG, Orozco ALS, Villalba LJG (2020) Image tampering detection by estimating interpolation patterns. Future Gener Comp Sy 107:229–237

    Article  Google Scholar 

  41. Warbhe AD, Dharaskar RV, Thakare VM (2016) A Scaling Robust Copy-Paste Tampering Detection for Digital Image Forensics. Procedia Comput Sci 79:458–465

    Article  Google Scholar 

  42. Xiang Y, Xiao D, Wang H, Li X (2019) A secure image tampering detection and self-recovery scheme using POB number system over cloud. Signal Process 162:282–295

    Article  Google Scholar 

  43. Yan C-P, Pun C-M, Yuan X-C (2016) Multi-scale image hashing using adaptive local feature extraction for robust tampering detection. Signal Process 121:1–16

    Article  Google Scholar 

  44. Zheng L, Zhang Y, Vrizlynn LL (2019) Thing, A survey on image tampering and its detection in real-world photos. J Vis Commun Image R 58:380–399

    Article  Google Scholar 

  45. Zhou W, Yu L, Wang Z, Wu M, Sun L (2016) Binocular visual characteristics based fragile watermarking scheme for tamper detection in stereoscopic images. AEU - Inter J Elect Commun 70(1):77–84

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication, National Institute of Technology, Srinagar, India

    Mohassin Ahmad

  2. Department of Electronics and Communication, National Institute of Technology, Srinagar, India

    Farida Khursheed

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  1. Mohassin Ahmad
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  2. Farida Khursheed
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Correspondence to Mohassin Ahmad.

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Ahmad, M., Khursheed, F. A novel image tamper detection approach by blending forensic tools and optimized CNN: Sealion customized firefly algorithm. Multimed Tools Appl 81, 2577–2601 (2022). https://doi.org/10.1007/s11042-021-11529-0

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