An improved median filtering anti-forensics with better image quality and forensic undetectability | Multidimensional Systems and Signal Processing Skip to main content
Springer Nature Link
Log in

An improved median filtering anti-forensics with better image quality and forensic undetectability

  • Published:
Multidimensional Systems and Signal Processing Aims and scope Submit manuscript

Abstract

Median filtering has received considerable attention and popularity for image enhancement and anti-forensics. It can be utilized as an image denoising and smoothing tool to disguise the footprints of image processing operations such as image resampling and JPEG compression. A two-step median filtering anti-forensic framework is proposed in this paper to fool the existing median filtering forensic detectors by hiding the median filtering artifacts. In the proposed framework, a variational deconvolution approach is initially employed to generate a median filtered forgery. Now, this forgery is further processed in the second step by considering Total Variation (TV) based minimization optimization problem to eradicate the median filtering artifacts left during deconvolution operation. Moreover, the proposed TV-based minimization algorithms significantly reduce the unnatural (grainy) noise left during the variational deconvolution. Two types of TV-based minimization problems are suggested, first relies on the TV of energy by considering the image gradient and second on the structure of a given image. The performance of the proposed scheme is evaluated by considering the worst-case and optimal scenarios. The experimental results based on UCID and BOSSBase dataset images demonstrate that the proposed anti-forensic methods provide superior results in terms of image visual quality and forensic undetectability as compared to the existing approaches, with slight increase in computational time.

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
¥17,985 /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 includes VAT (Japan)

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Bas, P., Filler, T., & Pevny, T. (2011). Break our steganographic system: the ins and outs of organizing BOSS. In Proceedings of the international conference on information hiding (pp. 59–70).

  • Bayar, B., & Stamm, M. C. (2018). Constrained convolutional neural networks: A new approach towards general purpose image manipulation detection. IEEE Transactions on Information Forensics and Security, 13(11), 2691–2706.

    Article  Google Scholar 

  • Bertalmio, M., Vese, L., Sapiro, G., & Osher, S. (2003). Simultaneous structure and texture image inpainting. IEEE Transactions on Image Processing, 12(8), 882–889.

    Article  Google Scholar 

  • Bi, X., & Pun, C. M. (2017). Fast reflective offset-guided searching method for copy-move forgery detection. Information Science, 418, 531–545.

    Article  Google Scholar 

  • Bi, X., Pun, C. M., & Yuan, X. C. (2016). Multi-level dense descriptor and hierarchical feature matching for copy-move forgery detection. Information Science, 345, 226–242.

    Article  Google Scholar 

  • Böhme, R., & Kirchner, M. (2013). Counter-forensics: Attacking image forensics. In H. T. Sencar & N. Memon (Eds.), Digital image forensics (pp. 327–366). New York: Springer-Verlag.

    Chapter  Google Scholar 

  • Bovik, A. C. (1987). Streaking in median filtered images. IEEE Transactions on Acoustics, Speech, Signal Processing, 35(4), 493–503.

    Article  MATH  Google Scholar 

  • Cao, G., Zhao, Y., Ni, R., Yu, L., & Tian, H. (2010). Forensic detection of median filtering in digital images. In Proceedings of the IEEE international conference on multimedia expo (pp. 89–94).

  • Chang, C. C., & Lin, C. J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3). Art. ID 27.

  • Chen, C., & Ni, J. (2011). Median filtering detection using edge based prediction matrix. In Proceedings of the 10th international workshop on digital forensics and watermarking (pp. 361–375).

  • Chen, C., Ni, J., & Huang, J. (2013). Blind detection of median filtering in digital images: A difference domain based approach. IEEE Transactions on Image Processing, 22(12), 4699–4710.

    Article  MathSciNet  MATH  Google Scholar 

  • Chen, C., Ni, J., Huang, R., & Huang, J. (2012). Blind median filtering detection using statistics in difference domain. In Proceeding of the 14th international conference on information hiding (pp. 1–15).

  • Chen, J., Kang, X., Liu, Y., & Wang, Z. J. (2015). Median filtering forensics based on convolutional neural networks. IEEE Signal Processing Letters, 22(11), 1849–1853.

    Article  Google Scholar 

  • Chen, Y., Lyu, Z., Kang, X., & Wang, Z. J. (2018). A rotation-invariant convolutional neural network for image enhancement forensics. In Proceedings of the IEEE international conference on acoustics, speech, and signal processing (pp. 2111–2115).

  • Dang-Nguyen, D. T., Gebru, I. D., Conotter, V., Boato, G., & De Natale, F. G. B. (2013). Counter-forensics of median filtering. In Proceeding of the IEEE 15th international workshop on multimedia signal processing (pp. 260–265).

  • Fan, W., Wang, K., Cayre, F., & Xiong, Z. (2013). JPEG anti-forensics using non-parametric DCT quantization noise estimation and natural image statistics. In Proceedings of the 1st ACM international workshop on information hiding and multimedia security (pp. 117–122).

  • Fan, W., Wang, K., Cayre, F., & Xiong, Z. (2013). A variational approach to JPEG anti-forensics. In Proceedings of the IEEE international conference on acoustics, speech, and signal processing (pp. 3058–3062).

  • Fan, W., Wang, K., Cayre, F., & Xiong, Z. (2015). Median filtered image quality enhancement and anti-forensics via variational deconvolution. IEEE Transactions on Information Forensics and Security, 10(5), 1076–1091.

    Article  Google Scholar 

  • Fan, Z., & de Queiroz, R. L. (2003). Identification of bitmap compression history: JPEG detection and quantizer estimation. IEEE Transactions on Image Processing, 12(2), 230–235.

    Article  Google Scholar 

  • Fontani, M., & Barni, M. (2012). Hiding traces of median filtering in digital images. In Proceeding of the 20th European signal processing conference (pp. 1239–1243).

  • Holub, V., & Fridrich, J. (2013). Digital image steganography using universal distortion. In Proceeding of the 1st ACM workshop on information hiding and multimedia security (pp. 59–68).

  • Kang, X., Stamm, M. C., Peng, A., & Liu, K. J. R. (2012). Robust median filtering forensics based on the autoregressive model of median filtered residual. In Proceedings of the Asia-Pacific signal and information processing association annual summit and conference (pp. 1–9).

  • Kang, X., Stamm, M. C., Peng, A., & Liu, K. J. R. (2013). Robust median filtering forensics using an autoregressive model. IEEE Transactions on Information Forensics and Security, 8(9), 1456–1468.

    Article  Google Scholar 

  • Kim, D., Jang, H., Mun, S., Choi, S., & Lee, H. (2018). Median filtered image restoration and anti-forensics using adversarial networks. IEEE Signal Processing Letters, 25(2), 278–282.

    Article  Google Scholar 

  • Kim, J. H., Akram, F., & Choi, K. N. (2017). Image denoising feedback framework using split Bregman approach. Expert Systems with Applications, 87, 252–266.

    Article  Google Scholar 

  • Kirchner, M., & Böhme, R. (2008). Hiding traces of resampling in digital images. IEEE Transactions on Information Forensics and Security, 3(4), 582–592.

    Article  Google Scholar 

  • Kirchner, M., & Fridrich, J. (2010). On detection of median filtering in digital images. In Proceedings SPIE (Vol. 7541, p. 754110).

  • Krishnan, D., & Fergus, R. (2009). Fast image deconvolution using hyper- Laplacian priors (pp. 1033–1041)., Advances in neural information processing systems Red Hook: Curran & Associates Inc.

    Google Scholar 

  • Krishnan, D., Tay, T., & Fergus, R. (2011). Blind deconvolution using a normalized sparsity measure. In Proceedings of the IEEE conference on computer vision and pattern Recognition (pp. 233–240).

  • Levin, A., Weiss, Y., Durand, F., & Freeman, W. T. (2009). Understanding and evaluating blind deconvolution algorithms. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1964–1971).

  • Li, H., Luo, W., Qiu, X., & Huang, J. (2018). Identification of various image operations using residual-based features. IEEE Transaction on Circuits and Systems for Video Technology, 28(1), 31–45.

    Article  Google Scholar 

  • Liu, A., Zhao, Z., Zhang, C., & Su, Y. (2017). Median filtering forensics in digital images based on frequency-domain features. Multimedia Tools and Applications, 76(21), 22119–22132.

    Article  Google Scholar 

  • Peng, A., & Kang, X. (2012). Robust median filtering detection based on filtered residual. In Proceeding of the 11th international workshop on digital forensics and watermarking (pp. 344–357).

  • Pevný, T., Bas, P., & Fridrich, J. (2010). Steganalysis by subtractive pixel adjacency matrix. IEEE Transactions on Information Forensics and Security, 5(2), 215–224.

    Article  Google Scholar 

  • Pevny, T., Filler, T., & Bas, P. (2010). Using high-dimensional image models to perform highly undetectable steganography. In Proceedings of the international workshop on information hiding (pp. 161–177).

  • Pitas, I., & Venetsanopoulos, A. N. (1992). Order statistics in digital image processing. Proceedings of the IEEE, 80(12), 1893–1921.

    Article  Google Scholar 

  • Qin, C., Chang, C. C., Huang, Y. H., & Liao, L. T. (2013). An inpainting-assisted reversible steganographic scheme using a histogram shifting mechanism. IEEE Transactions on Circuits and Systems for Video Technology, 23(7), 1109–1118.

    Article  Google Scholar 

  • Qin, C., He, Z., Yao, H., Cao, F., & Gao, L. (2018). Visible watermark removal scheme based on reversible data hiding and image inpainting. Signal Processing: Image Communication, 60, 160–172.

    Google Scholar 

  • Redi, J. A., & Dugelay, J. L. (2011). Digital image forensics: A booklet for beginners. Multimedia Tools and Applications, 51(1), 133–162.

    Article  Google Scholar 

  • Schaefer, G., & Stich, M. (2003). UCID: An uncompressed color image database. Proceeding of SPIE, 5307, 472–480.

    Article  Google Scholar 

  • Singh, G., & Singh, K. (2017). Improved JPEG anti-forensics with better image visual quality and forensic undetectability. Forensic Science International, 277, 133–147.

    Article  Google Scholar 

  • Singh, G., & Singh, K. (2018). Forensics for partially double compressed doctored JPEG images. Multimedia Tools and Applications, 77(1), 485–502.

    Article  Google Scholar 

  • Stamm, M. C., & Liu, K. J. R. (2011). Anti-forensics of digital image compression. IEEE Transactions on Information Forensics and Security, 6(3), 1050–1065.

    Article  Google Scholar 

  • Vapnik, V. (2013). The nature of statistical learning theory. New York: Springer.

    MATH  Google Scholar 

  • Wang, D., Gao, T., & Yang, F. (2018). A forensic algorithm against median filtering based on coefficients of image blocks in frequency domain. Multimedia Tools and Applications, 77(18), 23411–23427.

    Article  Google Scholar 

  • Wang, W., Zhao, X., & Ng, M. (2016). A cartoon-plus-texture image decomposition model for blind deconvolution. Multidimensional Systems and Signal Processing, 27(2), 541–562.

    Article  MathSciNet  MATH  Google Scholar 

  • Wu, Z.-H., Stamm, M. C., & Liu, K. J. R. (2013). Anti-forensics of median filtering. In Proceedings of the IEEE international conference on acoustics, speech, and signal processing (pp. 3043–3047).

  • You, Y. L., Xu, W., & Tannenaum, A. (1996). Behavioral analysis of anisotropic diffusions in image processing. IEEE Transactions on Image Processing, 5(11), 1539–1553.

    Article  Google Scholar 

  • Yu, S., Moon, B., Kim, D., Kim, S., Choe, W., Lee, S., et al. (2017). Continuous digital zooming of asymmetric dual camera images using registration and variational image restoration. Multidimensional Systems and Signal Processing, 1–29.

  • Yuan, H.-D. (2011). Blind forensics of median filtering in digital images. IEEE Transactions on Information Forensics and Security, 6(4), 1335–1345.

    Article  Google Scholar 

  • Zeng, H., Kang, X., & Peng, A. (2016). A Multi-purpose countermeasure against image anti-forensics using autoregressive model. Neurocomputing, 189, 117–122.

    Article  Google Scholar 

  • Zhang, Y., Li, S., Wang, S., & Shi, Y. Q. (2014). Revealing the traces of median filtering using high-order local ternary patterns. IEEE Signal Processing Letters, 21(3), 275–279.

    Article  Google Scholar 

  • Zhang, Y. S., Zhang, F., & Li, B. Z. (2018). Image restoration method based on fractional variable order differential. Multidimensional Systems and Signal Processing, 29(3), 999–1024.

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by Visvesvaraya PHD scheme for Electronics and IT, Ministry of Electronics and Information Technology, Government of India (Grant PhD-MLA/4(33)/2015-16/01).

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, TIET, Patiala, Punjab, India

    Kulbir Singh, Ankush Kansal & Gurinder Singh

Authors
  1. Kulbir Singh
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ankush Kansal
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Gurinder Singh
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Kulbir Singh.

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

Singh, K., Kansal, A. & Singh, G. An improved median filtering anti-forensics with better image quality and forensic undetectability. Multidim Syst Sign Process 30, 1951–1974 (2019). https://doi.org/10.1007/s11045-019-00637-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11045-019-00637-8

Keywords