Skip to main content
SpringerLink
Log in

A technique for image splicing detection using hybrid feature set

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

Abstract

Image manipulation has no longer been rocket science for non-professionals. Tampering of images has become so popular due to the accessibility of free editing application in smart phone’s store, these applications work without any agreement or license from the user which makes the condition more vulnerable. The image alteration is not limited to the smart phone’s applications, they can be done online without downloading and signing in the application making the scenario even worst. These forged images are so tricky that they are not predictable with bare human eyes. So, in order to tackle with this delinquent act, one must develop such system which can instantly discriminate between the unique and altered image. One of the best technologies that can tackle the problem and helps to develop such a scheme is Machine learning. There are several classification techniques based on the requirement of the system that can be applied to the data set, resulting in the classification of images under the groups forged and unforged images. In this work, we have discussed the images which are being forged using Image splicing Technique, in which the region of an original image is cropped and pasted onto the other original image. In this paper, a machine learning classification technique logistic regression has been used to classify images into two classes, spliced and non-spliced images. For this, a combination of four handcrafted features has been extracted from images for feature vector. Then these feature vectors are trained using logistic regression classification model. 10-fold cross-validation test evaluation procedure has been used to evaluate the result. Finally, the comparative analysis of the proposed method with other state-of-the-art methods on three online available datasets is presented in the paper. It is observed that the obtained results perform better than state-of-the-art methods.

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Abrahim AR, Rahim MSM, Bin SG (2018) Splicing image forgery identification based on artificial neural network approach and texture features. Cluster Comput:1–14. https://doi.org/10.1007/s10586-017-1668-8

  2. Adobe Sensi (n.d.). https://www.adobe.com/in/sensei.html. Accessed 19 Mar 2018

  3. Agarwal S, Chand S (2015) Image forgery detection using multi scale entropy filter and local phase quantization. Int J Image, Graph Signal Process 8:64–74. https://doi.org/10.5815/ijigsp.2015.10.08

    Article  Google Scholar 

  4. Alahmadi AA, Hussain M, Aboalsamh H, et al (2013) Splicing image forgery detection based on DCT and local binary pattern. Glob Conf Signal Inf Process IEEE 253–256

  5. Al-Qershi OM, Khoo BE (2018) Enhanced block-based copy-move forgery detection using k-means clustering. Multidimens Syst signal process 1–25. https://doi.org/10.1007/s11045-018-0624-y

  6. Amerini I, Becarelli R, Caldelli R (2014) Del Mastio a (2015) splicing forgeries localization through the use of first digit features. IEEE Int Work Inf Forensics Secur WIFS 2014:143–148. https://doi.org/10.1109/WIFS.2014.7084318

    Article  Google Scholar 

  7. Asghar K, Habib Z, Hussain M (2017) Copy-move and splicing image forgery detection and localization techniques: a review. Aust J Forensic Sci 49:281–307. https://doi.org/10.1080/00450618.2016.1153711

    Article  Google Scholar 

  8. Birajdar GK, Mankar VH (2013) Digital image forgery detection using passive techniques: a survey. Digit Investig 10:226–245. https://doi.org/10.1016/j.diin.2013.04.007

    Article  Google Scholar 

  9. Bunk J, Bappy JH, Mohammed TM, et al (2017) Detection and Localization of Image Forgeries Using Resampling Features and Deep Learning. IEEE Comput Soc Conf Comput Vis Pattern Recognit Work 2017-July:1881–1889. https://doi.org/10.1109/CVPRW.2017.235

  10. Dalal N (2005) Triggs W (2004) histograms of oriented gradients for human detection. IEEE Comput Soc Conf Comput Vis Pattern Recognit CVPR05 1:886–893. https://doi.org/10.1109/CVPR.2005.177

    Article  Google Scholar 

  11. Dong J, Wang W CASIA v1.0 and CASIA v2.0 image splicing dataset. In: Natl. Lab. Pattern recognition, Inst. Autom. Chinese Acad Sci Corel Image Database http://forensics.idealtest.org

  12. Doty M (2016) Misinformation in 2016: a timeline of fake news (photos). Wrap

    Google Scholar 

  13. FaceApp (n.d.) https://www.faceapp.com/. Accessed 19 Mar 2018

  14. Fan D, Zhang S, Wu Y, et al (2018) Face sketch synthesis style similarity : a new structure co-occurrence texture measure. arXiv Prepr arXiv180402975

  15. Fan DP, Gong C, Cao Y, et al (2018) Enhanced-alignment measure for binary foreground map evaluation. IJCAI

    Book  Google Scholar 

  16. Ferrara P, Bianchi T, De Rosa A, Piva A (2012) Image forgery localization via fine-grained analysis of CFA artifacts. IEEE Trans Inf Forensics Secur 7:1566–1577. https://doi.org/10.1109/TIFS.2012.2202227

    Article  Google Scholar 

  17. Fridrich J, Soukal D, Lukáš J (2003) Detection of copy-move forgery in digital images. Proc Digit Forensic Res Work. https://doi.org/10.1109/PACIIA.2008.240

  18. He Z, Lu W, Sun W, Huang J (2012) Digital image splicing detection based on Markov features in DCT and DWT domain. Pattern Recogn 45:4292–4299. https://doi.org/10.1016/j.patcog.2012.05.014

    Article  Google Scholar 

  19. Jaiswal AK, Srivastava R (2019) Image splicing detection using deep residual network. SSRN Electron J. https://doi.org/10.2139/ssrn.3351072

  20. Korus P (2017) Digital image integrity–a survey of protection and verification techniques. Digit Signal Process 71:1–26. https://doi.org/10.1016/j.dsp.2017.08.009

    Article  MathSciNet  Google Scholar 

  21. Korus P, Huang J (2016) Multi-scale fusion for improved localization of malicious tampering in digital images. IEEE Trans Image Process 25:1312–1326. https://doi.org/10.1109/TIP.2016.2518870

    Article  MathSciNet  MATH  Google Scholar 

  22. Kumar R, Srivastava R, Srivastava S (2015) Detection and classification of Cancer from microscopic biopsy images using clinically significant and biologically interpretable features. J Med Eng 2015:1–14. https://doi.org/10.1155/2015/457906

    Article  Google Scholar 

  23. Mahmood T, Irtaza A, Mehmood Z, Tariq Mahmood M (2017) Copy–move forgery detection through stationary wavelets and local binary pattern variance for forensic analysis in digital images. Forensic Sci Int 279:8–21. https://doi.org/10.1016/j.forsciint.2017.07.037

    Article  Google Scholar 

  24. Mahmood T, Mehmood Z, Shah M, Saba T (2018) A robust technique for copy-move forgery detection and localization in digital images via stationary wavelet and discrete cosine transform. J Vis Commun Image Represent 53:202–214. https://doi.org/10.1016/j.jvcir.2018.03.015

    Article  Google Scholar 

  25. Maigrot C, Kijak E, Sicre R, et al (2017) Tampering detection and localization in images from social networks : a CBIR approach. HAL id HAL-01623105

  26. Matlab RGb2Gray (n.d.) https://in.mathworks.com/help/matlab/ref/rgb2gray.html#description. Accessed 7 Jun 2018

  27. Muhammad G, Al-Hammadi MH, Hussain M, Bebis G (2014) Image forgery detection using steerable pyramid transform and local binary pattern. Mach Vis Appl 25:985–995. https://doi.org/10.1007/s00138-013-0547-4

    Article  Google Scholar 

  28. Ng T-T, Hsu J, Chang S-F Columbia image splicing detection evaluation dataset. In: DVMM lab. Columbia Univ CalPhotos Digit Libr

  29. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29:51–59. https://doi.org/10.1016/0031-3203(95)00067-4

    Article  Google Scholar 

  30. Parashar N, Tiwari N, Dubey D (2016) A survey of digital image tampering techniques. Int J Signal Process Image Process Pattern Recognit 9:415–420

    Google Scholar 

  31. Qazi T, Hayat K, Lin W et al (2013) Survey on blind image forgery detection. IET Image Process 7:660–670. https://doi.org/10.1049/iet-ipr.2012.0388

    Article  Google Scholar 

  32. Rao Y, Ni J (2017) A deep learning approach to detection of splicing and copy-move forgeries in images. 8th IEEE Int work Inf forensics Secur WIFS 2016 1–6. https://doi.org/10.1109/WIFS.2016.7823911

  33. Schetinger V, Oliveira MM, da Silva R, Carvalho TJ (2017) Humans are easily fooled by digital images. Comput Graph 68:142–151. https://doi.org/10.1016/j.cag.2017.08.010

    Article  Google Scholar 

  34. Shen C, Kasra M, Pan W et al (2019) Fake images: the effects of source, intermediary, and digital media literacy on contextual assessment of image credibility online. New Media Soc 21:438–463. https://doi.org/10.1177/1461444818799526

    Article  Google Scholar 

  35. Srivastava S, Sharma N, Singh SK, Srivastava R (2013) Design , analysis and classifier evaluation for a CAD tool for breast cancer detection from digital mammograms. Int J Biomed Eng Technol 13:270–300

  36. Viswanathan DG (2009) Features from Accelerated Segment Test (FAST). https://pdfs.semanticscholar.org/cd26/7a4b04d835dbecf01d47fc69ed3a38c23055.pdf.

  37. Wang W, Dong J, Tan T (2009) Effective image splicing detection based on image Chroma. IEEE International Conference on Image Processing, In, pp 1257–1260

    Google Scholar 

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

    Article  Google Scholar 

  39. Yu L, Han Q, Niu X (2016) Feature point-based copy-move forgery detection : covering the non-textured areas. Multimed Tools Appl 75:1159–1176. https://doi.org/10.1007/s11042-014-2362-y

    Article  Google Scholar 

  40. Zhang Z, Zhou Y, Kang J, Ren Y (2008) Study of image splicing detection. Int Conf Intell Comput Springer, Berlin, Heidelb:1103–1110. https://doi.org/10.1007/978-3-540-87442-3_136

  41. Zhang Y, Goh J, Win LL, Thing V (2016) Image region forgery detection: a deep learning approach. Cryptol Inf Secur Ser 14:1–11. https://doi.org/10.3233/978-1-61499-617-0-1

    Article  Google Scholar 

  42. Zhao X, Li J, Li S, Wang S (2010) Detecting digital image splicing in chroma spaces. Int Work Digit Watermarking:12–22

Download references

Author information

Authors and Affiliations

  1. Computing and Vision Lab, Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, 221005, India

    Ankit Kumar Jaiswal & Rajeev Srivastava

Authors
  1. Ankit Kumar Jaiswal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajeev Srivastava
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ankit Kumar Jaiswal.

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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jaiswal, A.K., Srivastava, R. A technique for image splicing detection using hybrid feature set. Multimed Tools Appl 79, 11837–11860 (2020). https://doi.org/10.1007/s11042-019-08480-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08480-6

Keywords

Search

Navigation