SpringerLink
Log in

An efficient method for image forgery detection based on trigonometric transforms and deep learning

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Image forgery detection is the basic key to solve many problems, especially social problems such as those in Facebook, and court cases. The common form of image forgery is the copy-move forgery, in which a section of the image is copied and pasted in another location within the same image. In this type of image forgery, it is easy to perform forgery, but more difficult to detect it, because the features of the copied parts are similar to those of the other parts of the image. This paper presents an approach for copy-move forgery detection based on block processing and feature extraction from the transforms of the blocks. In addition, a Convolutional Neural Network (CNN) is used for forgery detection. The feature extraction is implemented with serial pairs of convolution and pooling layers, and then classification between the original and tampered images is performed with and without transforms. A comparison study between different trigonometric transforms in 1D and 2D is presented for detecting the tampered parts in the image. This comparison study is based on the completeness rate for the detection. This comparison ensures that the DFT in 1D or 2D implementations is the best choice to detect copy-move forgery compared to other trigonometric transforms. In addition, the paper presents a comparison study between ten cases using the CNN learning technique to detect the manipulated image. The basic idea is to use a CNN to detect and extract features. The proposed CNN approach can also be used for active forgery detection because of its robustness to detect the manipulation of digital watermarked images or images with signatures.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Acharya T, Chakrabarti C (2006) A survey on lifting-based discrete wavelet transform architectures. Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology 42:321–339

    Article  MATH  Google Scholar 

  2. Amerini I, Serra G, Del Bimbo A, Caldelli R, Ballan L (2011) A sift-based forensic method for copy–move attack detection and transformation recovery. IEEE Transactions on Information Forensics and Security 6:1099–1110

    Article  Google Scholar 

  3. Amolins K, Dare P, Zhang Y (2007) Wavelet based image fusion techniques—an introduction, review and comparison. ISPRS J Photogramm Remote Sens 62:249–263

    Article  Google Scholar 

  4. Cao Y, Yang Q, Fan L, Gao T (2012) A robust detection algorithm for copy-move forgery in digital images. Forensic Sci Int 214:33–43

    Article  Google Scholar 

  5. Dang-Nguyen DT, Boato G, Conotter V, Pasquini C (2015) RAISE: a raw images dataset for digital image forensics. In Proceedings of the 6th ACM multimedia systems conference, pp. 219–224

  6. Farid H, Popescu A (2004) Exposing digital forgeries by detecting duplicated image regions. Department Computer Science, Dartmouth College, Technology Report TR2004–515

  7. Fridrich AJ, Lukáš AJ, Soukal BD (2003) Detection of copy-move forgery in digital images. In Proceedings of digital forensic research workshop

  8. Fridrich A et al (2003) Detection of copy-move forgery in digital images

  9. Graps A (1995) An introduction to wavelets. IEEE Comput Sci Eng 2:50–61

    Article  Google Scholar 

  10. Hafed ZM, Levine MD (2001) Face recognition using the discrete cosine transform. Int J Comput Vis 43:167–188

    Article  MATH  Google Scholar 

  11. Huang Y, Long D, Sun W, Lu W (2011) Improved DCT-based detection of copy-move forgery in images. Forensic Sci Int 206:178–184

    Article  Google Scholar 

  12. Huh J-H (2018) Big data analysis for personalized health activities: machine learning processing for automatic keyword extraction approach. Symmetry 10(4):93

    Article  MathSciNet  Google Scholar 

  13. Jaberi M, Muhammad G, Hussain M, Bebis G (2014) Accurate and robust localization of duplicated region in copy–move image forgery. Mach Vis Appl 25:451–475

    Article  Google Scholar 

  14. Jung S-H, Huh J-H (2019) A novel on transmission line tower big data analysis model using altered K-means and ADQL. Sustainability 11(13):3499

    Article  Google Scholar 

  15. Kim P (2017) Matlab deep learning. With Machine Learning, Neural Networks and Artificial Intelligence

  16. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. ar**v preprint ar**v:1412.6980

  17. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  18. Li G, Wu Q, Sun S, Tu D (2007) A sorted neighborhood approach for detecting duplicated regions in image forgeries based on DWT and SVD. In Multimedia and expo, 2007 IEEE international conference on, pp. 1750–1753

  19. Li L, Wu X, Zhu H, Li S (2014) Detecting copy-move forgery under affine transforms for image forensics. Comput Electr Eng 40:1951–1962

    Article  Google Scholar 

  20. Litjens G et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

    Article  Google Scholar 

  21. Lynch G, Liao H-YM, Shih FY (2013) An efficient expanding block algorithm for image copy-move forgery detection. Inf Sci 239:253–265

    Article  Google Scholar 

  22. Mahdian B, Saic S (2007) Detection of copy–move forgery using a method based on blur moment invariants. Forensic Sci Int 171:180–189

    Article  Google Scholar 

  23. Nath V, Gaharwar R, Gaharwar G (2015) Comprehensive study of different types image forgeries. International Conference of Recent Advances Engineering Science and Management, New Delhi

    Google Scholar 

  24. Nishanth K, Karthik G (2015) Identification of diabetic maculopathy stages using fundus images. J Mol Image Dynamic

  25. Patil SS, Khade BS, Dhongde JD, Patil NP, Patil AN (2014) Digital image forgery detection using basic manipulations in Facebook. Int J Sci Technol Res 3:356–359

    Google Scholar 

  26. Popescu A, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions, Dept. Comput. Sci., Dartmouth College, Tech. Rep. TR2004–515

  27. Ranzato MA, Huang FJ, Boureau YL, LeCun Y (2007). Unsupervised learning of invariant feature hierarchies with applications to object recognition. In Computer vision and pattern recognition, 2007. CVPR'07. IEEE conference on (pp. 1–8). IEEE.

  28. Schmidhuber J (2015) Deep learning in neural networks: An overview. Neural Netw 61:85–117

    Article  Google Scholar 

  29. Seo Y-S, Huh J-H (2019) Automatic emotion-based music classification for supporting intelligent IoT applications. Electronics 8(2):164

    Article  Google Scholar 

  30. Shen D, Wu G, Suk H-I (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248

    Article  Google Scholar 

  31. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  32. Wang Z-Q, Tan K, Wang D (2019) Deep learning based phase reconstruction for speaker separation: a trigonometric perspective. In: ICASSP 2019–2019 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, p. 71–75

  33. Warif NBA, Choo KKR, Wahab AWA, Shamshirband S, Ramli R, Salleh R, Idris MYI (2016) Copy-move forgery detection: survey, challenges and future directions. J Netw Comput Appl 75:259–278

    Article  Google Scholar 

  34. Winograd S (1978) On computing the discrete Fourier transform. Math Comput:175–199

  35. Zandi M, Mansouri A, Mahmoudi-Aznaveh A (2014) Adaptive matching for copy-move forgery detection. In Information forensics and security (WIFS), 2014 IEEE international workshop on, pp. 119–124

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Faten Maher Al_Azrak, Moawad I. Dessowky, Ahmed S. Elkorany & Fathi E. Abd. El-Samie

  2. Department of the Robotics and Inteligent Machines, Faculty of Artificial Inteligence, Kafrelsheikh University, Kafrelsheikh, Egypt

    Ahmed Sedik

  3. Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Ghada M. El Banby

  4. Department of Electrical Engineering, Electronics and Communications Engineering, Faculty of Engineering, Minia University, Minia, 61111, Egypt

    Ashraf A. M. Khalaf

Authors
  1. Faten Maher Al_Azrak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ahmed Sedik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Moawad I. Dessowky
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ghada M. El Banby
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ashraf A. M. Khalaf
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ahmed S. Elkorany
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Fathi E. Abd. El-Samie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fathi E. Abd. El-Samie.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Al_Azrak, F.M., Sedik, A., Dessowky, M.I. et al. An efficient method for image forgery detection based on trigonometric transforms and deep learning. Multimed Tools Appl 79, 18221–18243 (2020). https://doi.org/10.1007/s11042-019-08162-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08162-3

Keywords

Search

Navigation