Soft Computing

, Volume 21, Issue 19, pp 5693–5701 | Cite as

Detection of content-aware image resizing based on Benford’s law

  • Guorui Sheng
  • Tao Li
  • Qingtang Su
  • Beijing Chen
  • Yi Tang
Methodologies and Application
  • 170 Downloads

Abstract

Content-aware image resizing is currently widely used because it maintains the original appearance of important objects to the greatest extent when the aspect ratio of an image changes during resizing. Content-aware image resizing techniques, such as seam carving, are also used for image forgery. A new Benford’s law-based algorithm for detecting content-aware resized images is presented. The algorithm extracts features on the basis of the first digit distribution of the discrete cosine transform coefficients, which follow the standard Benford’s law. We trained these features from both normal images and content-aware resized images using a support vector machine. The experimental results show that the proposed method can efficiently distinguish a content-aware resized image from a normal image, and its precision is better than that of existing methods, including those based on Markov features and others.

Keywords

Content-aware image resizing Image forensics Image forgery Seam carving SVM Benford’s law 

References

  1. Amerini I, Ballan L, Caldelli R, Del Bimbo A, Del Tongo L, Serra G (2013) Copy-move forgery detection and localization by means of robust clustering with j-linkage. Signal Process Image Commun 28(6):659–669Google Scholar
  2. Amerini I, Ballan L, Caldelli R, Del Bimbo A, Serra G (2011) A sift-based forensic method for copy-move attack detection and transformation recovery. IEEE Trans Inf Forens Secur 6(3):1099–1110Google Scholar
  3. Avidan S, Shamir A (2007) Seam carving for content-aware image resizing. In: ACM transactions on graphics (TOG), vol 26. ACM, New York, pp 10Google Scholar
  4. Benford F (1938) The law of anomalous numbers. In: Proceedings of the American Philosophical Society, pp 551–572Google Scholar
  5. Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27Google Scholar
  6. Chi-Yao W, Hong Zhang Y, Chun Lin L, Wang SJ (2013) Visible watermarking images in high quality of data hiding. J Supercomput 66(2):1033–1048Google Scholar
  7. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297Google Scholar
  8. Fillion C, Sharma G (2010) Detecting content adaptive scaling of images for forensic applications. In: IS&T/SPIE electronic imaging. International Society for Optics and Photonics, San Francisco, pp 75410Z–75410ZGoogle Scholar
  9. Fridrich AJ, Soukal BD, Lukáš AJ (2003) Detection of copy-move forgery in digital images. In: Proceedings of digital forensic research workshop, CiteseerGoogle Scholar
  10. Fu D, Shi YQ, Su W (2006) Detection of image splicing based on Hilbert-Huang transform and moments of characteristic functions with wavelet decomposition. In: Digital Watermarking. Springer, Berlin, pp 177–187Google Scholar
  11. Hill TP (1995) A statistical derivation of the significant-digit law. In: Statistical science, pp 354–363Google Scholar
  12. Jin Li, Qian Wang, Cong Wang, Kui Ren (2011) Enhancing attribute-based encryption with attribute hierarchy. Mob Netw Appl 16(5):553–561CrossRefGoogle Scholar
  13. Li J, Chen X, Li M, Li J, Lee PC, Lou W (2014) Secure deduplication with efficient and reliable convergent key management. In: IEEE transactions on parallel and distributed systems 25(6):1615–1625Google Scholar
  14. Li J, Kim K (2010) Hidden attribute-based signatures without anonymity revocation. Inf Sci 180(9):1681–1689Google Scholar
  15. Li J, Kim K, Zhang F, Chen X (2007) Aggregate proxy signature and verifiably encrypted proxy signature. In: Provable security. Springer, Berlin, pp 208–217Google Scholar
  16. Li J, Wang Q, Wang C, Cao N, Ren K, Lou W (2010) Fuzzy keyword search over encrypted data in cloud computing. In: INFOCOM, 2010 Proceedings IEEE. IEEE, New York, pp 1–5Google Scholar
  17. Lu W, Wu M (2011) Seam carving estimation using forensic hash. In: Proceedings of the thirteenth ACM multimedia workshop on multimedia and security. ACM, New York, pp 9–14Google Scholar
  18. Sarkar A, Nataraj L, Manjunath BS (2009) Detection of seam carving and localization of seam insertions in digital images. In: Proceedings of the 11th ACM workshop on multimedia and security. ACM, New York, pp 107–116Google Scholar
  19. Shi YQ, Chen C, Chen W (2007) A natural image model approach to splicing detection. In: Proceedings of the 9th workshop on multimedia & security. ACM, New York, pp 51–62Google Scholar
  20. Simon Newcomb (1881) Note on the frequency of use of the different digits in natural numbers. Am J Math 4(1):39–40MathSciNetGoogle Scholar
  21. Tian-Tsong N, Shih-Fu C, Sun Q (2004) A data set of authentic and spliced image blocks. Columbia University, ADVENT Technical Report, p 203Google Scholar
  22. Tsung-Yuan Liu, Wen-Hsiang Tsai (2010) Generic lossless visible watermarking new approach. IEEE Trans Image Process 19(5):1224–1235MathSciNetCrossRefGoogle Scholar
  23. Wang J, Ma H, Tang Q, Li J, Zhu H, Ma S, Chen X (2013) Efficient verifiable fuzzy keyword search over encrypted data in cloud computing. Comput Sci Inf Syst 10(2):667–684Google Scholar
  24. Yilei W, Wong DS, Zhao C, Xu Q (2015) Fair two-party computation with rational parties holding private types. Security and communication. Networks 8(2):284–297Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Guorui Sheng
    • 1
  • Tao Li
    • 1
  • Qingtang Su
    • 1
  • Beijing Chen
    • 2
  • Yi Tang
    • 3
  1. 1.School of Information Science and Electrical EngineeringLudong UniversityYantaiChina
  2. 2.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina
  3. 3.Department of MathematicsGuangzhou UniversityGuangzhouChina

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