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


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.


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



This study was funded by National Natural Science Foundation of China (No. 61502218, 61232016, U1405254), Outstanding Young Scientists Foundation Grant of Shandong Province (No. BS2014DX016), Natural Science Foundation of Shandong Province (ZR2014FM005, ZR2013 FL008), Shandong Province Higher Educational Science and Technology Program (J14LN20), Shandong Province Science and Technology Plan Projects (2014 GGB01944, 2015GSF116001), Doctoral Foundation of Lu dong University (LY2014034, LY2013005, LY2015033), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Guangzhou Scholars Project (No. 1201561613).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.


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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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