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Differentiating digital image forensics and tampering localization by a novel hybrid approach

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Abstract

Digital images are very necessary for various fields because of the availability of software and applications, which can create the images as looking reality. Moreover, the categorization of Computer-generated Images (CGI) from Natural Images (NI) is difficult because it can’t be differentiated by the human eyes. So, this research developed a novel Knowledge-based Fuzzy Approximation (KBFA) model for differentiating CGI, NI, and Spliced Images (SI). Subsequently, Hybrid Grey Wolf Ant Lion (H-GWAL) optimization approach is developed to the localization of tampered region in a spliced image. Moreover, the proposed H-GWAL algorithm has been utilized for enhancing the classification accuracy of the proposed method. Hence, this method distinguishes the CGI from NI and SI from original images and the classified images are detected by the proposed KBFA with H-GWAL model. Additionally, this method is simulated using Python and the obtained results prove the performance of an innovative method. Moreover, the obtained results in terms of detection accuracy, precision, recall, and F1-measure are compared with recent existing approaches.

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References

  1. Abdel-Basset M, El-Shahat D et al (2020) A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection. Expert Syst Appl 139:112824. https://doi.org/10.1016/j.eswa.2019.112824

    Article  Google Scholar 

  2. Abrahim AR, Rahim MSM, Sulong GB (2019) Splicing image forgery identification based on artificial neural network approach and texture features. Cluster Comput 22(1):647–660. https://doi.org/10.1007/s10586-017-1668-8

    Article  Google Scholar 

  3. Alipour N, Behrad A (2020) Semantic segmentation of JPEG blocks using a deep CNN for non-aligned JPEG forgery detection and localization. Multimed Tools Appl 1–17. https://doi.org/10.1007/s11042-019-08597-8

  4. 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. https://doi.org/10.1016/j.ijleo.2019.04.074

    Article  Google Scholar 

  5. Dong J, Wang W, Tan T (2013) Casia image tampering detection evaluation database. 2013 IEEE China summit and international conference on signal and information processing, IEEE. https://doi.org/10.1109/ChinaSIP.2013.6625374

  6. Jaiswal AK, Srivastava R (2020) A technique for image splicing detection using hybrid feature set. Multimed Tools Appl 1–24. https://doi.org/10.1007/s11042-019-08480-6

  7. Kabbai L, Abdellaoui M, Douik A (2019) Image classification by combining local and global features. Vis Comput 35(5):679–693. https://doi.org/10.1007/s00371-018-1503-0

    Article  Google Scholar 

  8. Kanwal N, Girdhar A, Kaur L, Bhullar JS (2020) Digital image splicing detection technique using optimal threshold based local ternary pattern. Multimed Tools Appl 1–18. https://doi.org/10.1007/s11042-020-08621-2

  9. Kilic H, Yuzgec U, Karakuzu C (2020) A novel improved antlion optimizer algorithm and its comparative performance. Neural Comput & Applic 32(8):3803–3824. https://doi.org/10.1007/s00521-018-3871-9

    Article  Google Scholar 

  10. Liu Y, Zhu X, Zhao X, Cao Y (2019) Adversarial learning for constrained image splicing detection and localization based on atrous convolution. IEEE Trans Inf For Sec 14(10):2551–2566. https://doi.org/10.1109/TIFS.2019.2902826

    Article  Google Scholar 

  11. Long M, Peng F, Zhu Y (2019) Identifying natural images and computer generated graphics based on binary similarity measures of PRNU. Multimed Tools Appl 78(1):489–506. https://doi.org/10.1007/s11042-017-5101-3

    Article  Google Scholar 

  12. Özgen AC, Fasounaki M, Ekenel HK (2018) Text detection in natural and computer-generated images. 2018 26th signal processing and communications applications conference (SIU), IEEE. https://doi.org/10.1109/SIU.2018.8404600

  13. Pham NT, Lee JW, Kwon GR, Park CS (2019) Efficient image splicing detection algorithm based on markov features. Multimed Tools Appl 78(9):12405–12419. https://doi.org/10.1007/s11042-018-6792-9

    Article  Google Scholar 

  14. Quan W, Wang K, Yan DM, Zhang X (2018) Distinguishing between natural and computer-generated images using convolutional neural networks. IEEE Trans Inf For Secur 13(11):2772–2787. https://doi.org/10.1109/TIFS.2018.2834147

    Article  Google Scholar 

  15. Rao Y, Ni J, Zhao H (2020) Deep learning local descriptor for image splicing detection and localization. IEEE Access 8:25611–25625. https://doi.org/10.1109/ACCESS.2020.2970735

    Article  Google Scholar 

  16. Salloum R, Ren Y, Kuo CCJ (2018) Image splicing localization using a multi-task fully convolutional network (MFCN). J Vis Commun Image Represent 51:201–209. https://doi.org/10.1016/j.jvcir.2018.01.010

    Article  Google Scholar 

  17. 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. https://doi.org/10.1016/j.ijleo.2018.07.021

    Article  Google Scholar 

  18. Sharma S, Ghanekar U (2019) Spliced image classification and tampered region localization using local directional pattern. Int J Image Graphics Signal Process 11(3)

  19. Soni R, Kumar B, Chand S (2019) Optimal feature and classifier selection for text region classification in natural scene images using Weka tool. Multimed Tools Appl 78(22):31757–31791. https://doi.org/10.1007/s11042-019-07998-z

    Article  Google Scholar 

  20. Tan DQ, Shen XJ, Qin J, Chen HP (2016) Detecting computer generated images based on local ternary count. Pattern Recognit Image Anal 26(4):720–725. https://doi.org/10.1134/S1054661816040167

    Article  Google Scholar 

  21. Tim SCW, Rombaut M, Pellerin D, Dutt A (2020) Descriptor extraction based on a multilayer dictionary architecture for classification of natural images. Comput Vis Image Underst 191:102708. https://doi.org/10.1016/j.cviu.2018.08.002

    Article  Google Scholar 

  22. Vega EAA, Fernández EG, Orozco ALS et al (2020) Image tampering detection by estimating interpolation patterns. Future Gener Comput Syst 107:229–237. https://doi.org/10.1016/j.future.2020.01.016

    Article  Google Scholar 

  23. Wang J, Li T, Luo X, Shi YQ, Jha SK (2018) Identifying computer generated images based on quaternion central moments in color quaternion wavelet domain. IEEE Trans Circuits Syst Video Technol 29(9):2775–2785. https://doi.org/10.1109/TCSVT.2018.2867786

    Article  Google Scholar 

  24. Xiao B, Wei Y, Bi X, Li W, Ma J (2020) Image splicing forgery detection combining coarse to refined convolutional neural network and adaptive clustering. Inf Sci 511:172–191. https://doi.org/10.1016/j.ins.2019.09.038

    Article  MathSciNet  Google Scholar 

  25. Xue F, Lu W, Ye Z, Liu H (2019) JPEG image tampering localization based on normalized gray level co-occurrence matrix. Multimed Tools Appl 78:9895–9918. https://doi.org/10.1007/s11042-018-6611-3

    Article  Google Scholar 

  26. Zeng H, Zhan Y, Kang X, Lin X (2017) Image splicing localization using PCA-based noise level estimation. Multimed Tools Appl 76(4):4783–4799. https://doi.org/10.1007/s11042-016-3712-8

    Article  Google Scholar 

  27. Zhang RS, Quan WZ, Fan LB, Hu LM, Yan DM (2020) Distinguishing computer-generated images from natural images using channel and pixel correlation. J Comput Sci Technol 35:592–602. https://doi.org/10.1007/s11390-020-0216-9

    Article  Google Scholar 

  28. Zhang W, Tang X, Yang Z, Niu S (2019) Multi-scale segmentation strategies in PRNU-based image tampering localization. Multimed Tools Appl 78:20113–20132. https://doi.org/10.1007/s11042-019-7288-y

    Article  Google Scholar 

  29. Zheng L, Zhang Y, Thing VLL (2019) A survey on image tampering and its detection in real-world photos. J Vis Commun Image Represent 58:380–399. https://doi.org/10.1016/j.jvcir.2018.12.022

    Article  Google Scholar 

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Authors and Affiliations

  1. ECE Department, GITAM (Deemed be University), Visakhapatnam, Andhra Pradesh, 530045, India

    Alluvenkateswara Rao & Dharma Raj Cheruku

  2. ECE Department, UCEV, JNTUK, Vizianagaram, Andhra Pradesh, 535003, India

    Chanamallu Srinivasa Rao

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  1. Alluvenkateswara Rao
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Correspondence to Alluvenkateswara Rao.

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Dharma Raj Cheruku deceased

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Rao, A., Rao, C.S. & Cheruku, D.R. Differentiating digital image forensics and tampering localization by a novel hybrid approach. Multimed Tools Appl 81, 18693–18713 (2022). https://doi.org/10.1007/s11042-022-12257-9

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  • DOI: https://doi.org/10.1007/s11042-022-12257-9

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