Skip to main content
SpringerLink
Log in

A sequential convolutional neural network for image forgery detection

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

Abstract

In this digital era, images are the major information carriers of contemporary society. Several multimedia manipulation tools like CorelDRAW, GIMP, Freehand, Adobe Photoshop, etc. are being used to forge the visual media for malicious reasons. It is becoming increasingly difficult to distinguish forged images from pristine images as a result of new manipulation techniques that have emerged over the past time. The most intriguing area of multimedia forensics research is image forgery detection. In the field of forensic image analysis, the most important task is to verify the authenticity of digital media. A novel passive approach for detecting digital image forgery is proffered in this manuscript. It is a sequential framework that uses a deep convolutional neural network to differentiate between original and altered images. On the COVERAGE dataset, numerous experiments have been evaluated in order to construct an effective and robust model, achieveing AUC value of 0.85 and F-measure of 0.70. The comparative results have been represented in summarized form and the results perform better than the state-of-the-art techniques.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Data Availability

The datasets generated during and/or analysed during the current study are available in the git repository, https://github.com/wenbihan/coverage.

References

  1. Verdoliva L (2020) Media forensics and deepfakes: an overview. IEEE J Sel Top Signal Process 14(5):910–932

    Article  Google Scholar 

  2. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde- Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial networks. arXiv preprint arXiv:1406.2661, 1(1):910–932

  3. Fridrich AJ, Soukal BD, Lukáš AJ (2003) Detection of copymove forgery in digital images, in in Proceedings of Digital Forensic Research Workshop, vol 1, no 1. Citeseer, p 403

  4. Redi JA, Taktak W, Dugelay J-L (2011) Digital image forensics: a booklet for beginners. Multimedia Tools Appl 51(1):133–162

    Article  Google Scholar 

  5. Bharti CN, Tandel P (2016) A survey of image forgery detection techniques. In: 2016 International conference on wireless communications, signal processing and networking (WiSPNET), vol 1, no 1. IEEE, pp 877–881

  6. Kaur S, Rani R (2022) Image forgery detection using multi-layer convolutional neural network. in Advanced Machine Intelligence and Signal Processing. Springer, pp 855–866

  7. Fekri-Ershad S, Alsaffar MF (2023) Developing a tuned three-layer perceptron fed with trained deep convolutional neural networks for cervical cancer diagnosis. Diagnostics 13(4):686

    Article  Google Scholar 

  8. Al Azrak FM, Sedik A, Dessowky MI, El Banby GM, Khalaf AA, Elkorany AS, El–Samie FEA (2020) An efficient method for image forgery detection based on trigonometric transforms and deep learning. Multimedia Tools Appl 79(25):18 221–18 243

  9. Farid H (2009) Image forgery detection. IEEE Signal Process Mag 26(2):16–25

    Article  Google Scholar 

  10. Hu W-C, Chen W-H, Huang D-Y, Yang C-Y (2016) Effective image forgery detection of tampered foreground or background image based on image watermarking and alpha mattes. Multimedia Tools Appl 75(6):3495–3516

    Article  Google Scholar 

  11. Ali SS, Ganapathi II, Vu N-S, Ali SD, Saxena N, Werghi N (2022) Image forgery detection using deep learning by recompressing images. Electronics 11(3):403

    Article  Google Scholar 

  12. Kaur S, Rani R, Garg R, Sharma N (2022) State-of-the-art techniques for passive image forgery detection: a brief review. Int J Electron Secur Digit Forensics 14(5):456–473

    Article  Google Scholar 

  13. Birajdar GK, Mankar VH (2013) Digital image forgery detection using passive techniques: A survey. Digit Investig 10(3):226–245

    Article  Google Scholar 

  14. Thakur R, Rohilla R (2020) Recent advances in digital image manipulation detection techniques: A brief review. Forensic Sci Int 1(1):110311

    Article  Google Scholar 

  15. Elaskily MA, Aslan HK, Elshakankiry OA, Faragallah OS, Abd El-Samie FE, Dessouky MM (2017) Comparative study of copymove forgery detection techniques. In: 2017 Intl Conf on Advanced Control Circuits Systems (ACCS) Systems & 2017 Intl Conf on New Paradigms in Electronics & Information Technology (PEIT), vol 1, no 1. IEEE, pp 193–203

  16. Bourouis S, Alroobaea R, Alharbi AM, Andejany M, Rubaiee S (2020) Recent advances in digital multimedia tampering detection for forensics analysis. Symmetry 12(11):1811

    Article  Google Scholar 

  17. Elaskily MA, Elnemr HA, Sedik A, Dessouky MM, El Banby GM, Elshakankiry OA, Khalaf AA, Aslan HK, Faragallah OS, Abd El-Samie FE (2020) A novel deep learning framework for copy-moveforgery detection in images. Multimedia Tools Appl 1(1):1–26

    Google Scholar 

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

    Article  Google Scholar 

  19. Zhu Y, Chen C, Yan G, Guo Y, Dong Y (2020) Ar-net: Adaptive attention and residual refinement network for copy-move forgery detection. IEEE Trans Ind Inform 16(10):6714–6723

    Article  Google Scholar 

  20. Zhou P, Han X, Morariu VI, Davis LS (2018) Learning rich features for image manipulation detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1(1):1053–1061

    Google Scholar 

  21. Bappy JH, Simons C, Nataraj L, Manjunath B, Roy-Chowdhury AK (2019) Hybrid lstm and encoder-decoder architecture for detection of image forgeries. IEEE Trans Image Process 28(7):3286–3300

    Article  MathSciNet  Google Scholar 

  22. Agarwal R, Verma OP (2019) An efficient copy move forgery detection using deep learning feature extraction and matching algorithm. Multimedia Tools Appl 1(1):1–22

    Google Scholar 

  23. Abdalla Y, Iqbal MT, Shehata M (2019) Copy-move forgery detection and localization using a generative adversarial network and convolutional neural-network. Information 10(9):286

    Article  Google Scholar 

  24. Kumar S, Mukherjee S, Pal AK (2023) An improved reduced featurebased copy-move forgery detection technique. Multimedia Tools Appl 82(1):1431–1456

    Article  Google Scholar 

  25. Wang X-Y, Wang X-Q, Niu P-P, Yang H-Y (2023) Accurate and robust image copy-move forgery detection using adaptive keypoints and fqgpcet-glcm feature. Multimedia Tools Appl 1–33

  26. Babu ST, Rao CS (2023) Efficient detection of copy-move forgery using polar complex exponential transform and gradient direction pattern. Multimedia Tools Appl 82(7):10061–10075

    Article  Google Scholar 

  27. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 1(1):403

  28. Wen B, Zhu Y, Subramanian R, Ng T-T, Shen X, Winkler S (2016) Coverage–a novel database for copy-move forgery detection. In: 2016 IEEE international conference on image processing (ICIP), 1(1). IEEE, pp 161–165

  29. Ferrara P, Bianchi T, De Rosa A, Piva A (2012) Image forgery localization via fine-grained analysis of cfa artifacts. IEEE Transactions on Information Forensics and Security 7(5):1566–1577

    Article  Google Scholar 

  30. Mahdian B, Saic S (2009) Using noise inconsistencies for blind image forensics. Image Vision Comput 27(10):1497–1503

    Article  Google Scholar 

  31. Bappy JH, Roy-Chowdhury AK, Bunk J, Nataraj L, Manjunath B (2017) Exploiting spatial structure for localizing manipulated image regions. Proceedings of the IEEE international conference on computer vision 1(1):4970–4979

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. GNA University, Phagwara, Punjab, India

    Simranjot Kaur, Sumit Chopra, Anchal Nayyar, Rajesh Sharma & Gagandeep Singh

Authors
  1. Simranjot Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sumit Chopra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anchal Nayyar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rajesh Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gagandeep Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Simranjot Kaur.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaur, S., Chopra, S., Nayyar, A. et al. A sequential convolutional neural network for image forgery detection. Multimed Tools Appl 83, 41311–41325 (2024). https://doi.org/10.1007/s11042-023-17028-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-17028-8

Keywords

Search

Navigation