Hopfield network-based approach to detect seam-carved images and identify tampered regions
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Seam carving is a content-aware algorithm for image resizing and tampering. This algorithm assigns an energy map to an image and removes the seams with low energy from the image. By doing this, seam carving makes it possible to reduce the image size and eliminate specific content from images. The detection of seam carving has lately been an important but challenging area of research. In past work, we had proposed a method that involved analyzing optimal patch types to recover seams and thus to detect seam-carved images. This method yielded highly accurate detection results. In this paper, we introduce an auto-associated Hopfield network to determine the optimal patch type for seam recovery. We use the Hebbian learning rule to choose, among candidate patch types, the one that most closely resembles the relevant target pattern. Experiments showed that the retrieval process usually converged within eight iterations and that the converged patterns improved the detection accuracy, e.g., with rates of 95.97 and 98.55% for 20 and 50% seam-carved images respectively. We also used this enhanced patch analysis method to identify the seam-carved regions of a tampered image. Its accuracy for the identification of tampered regions was higher than 70% for images with < 30% seam carving.
KeywordsSeam carving Hopfield network Hebbian learning Digital forensics
This work was supported in part by the Ministry of Science and Technology, Taiwan, R.O.C. (Grant Nos. MOST 103-2221-E-182-049 and MOST-106-2221-E-182-075) and Chang Gung Memorial Hospital (Grant No. BMRPB21).
Compliance with ethical standards
Conflict of interest
The authors declare no potential conflicts of interest with respect to the research, authorship, and publication of this article.
- 1.Dong W, Zhou N, Paul JC, Zhang X (2009) Optimized image resizing using seam carving and scaling. ACM Trans Graph 28(10):125:1–125:10Google Scholar
- 3.Fillion C, Sharma G (2010) Detecting content adaptive scaling of images for forensic applications. Proc SPIE: Media Forensics Secur 7541:36–47Google Scholar
- 5.Gonzalez RC, Woods RE (2001) Digital image processing, 2nd edn. Prentice Hall, Englewood CliffsGoogle Scholar
- 9.Li ZN, Drew MS, Liu J (2014) Image compression standards. In: Fundamentals of multimedia, 2 edn, chap. 9. Springer, Berlin, pp 281–315Google Scholar
- 11.Liu Q, Chen Z (2014) Improved approaches with calibrated neighboring joint density to steganalysis and seam-carved forgery detection in jpeg images. ACM Trans Intell Syst Technol 5(4):63:1–63:30Google Scholar
- 13.Lu W, Varna AL, Wu M (2010) Forensic hash for multimedia information. In: SPIE Media Forensics and Security, pp 75410–75419Google Scholar
- 18.Sarkar A, Nataraj L, Manjunath BS (2009) Detection of seam carving and localization of seam insertions in digital images. In: Proceedings of 11th ACM workshop on multimedia and security, pp 107–116Google Scholar
- 19.Schaefer G, Stich M (2003) UCID: an uncompressed color image database. In: Proceedings of SPIE 5307, pp 472–480Google Scholar
- 21.Sheng G, Li T, Su Q, Chen B, Tang Y (2016) Detection of content-aware image resizing based on benfords law. Soft Computing, pp 1–9Google Scholar
- 22.Shi Y.Q, Chen C, Chen W (2006) A markov process based approach to effective attacking jpeg steganography. In: Lecture notes in computer science, pp 249–264Google Scholar
- 23.Wang Y, Liu J, Li Y, Yan J, Lu H (2016) Objectness-aware semantic segmentation. In: Proceedings of the 2016 ACM on multimedia conference (ACM MM 2016), pp 307–311Google Scholar
- 26.Wei JD, Lin YJ, Wu YJ, Kang LW (2013) A patch analysis approach for seam carved image detection. In: Proceedings of 40th international conference and exhibition on computer graphics and interactive techniques (ACM SIGGRAPH 2013)Google Scholar
- 27.Yan B, Yang X, Li K (2014) Efficient image retargeting via adaptive pixel fusion. In: Proceedings of ACM international conference on multimedia, pp 929–932Google Scholar
- 28.Ye J, Shi YQ (2017) An effective method to detect seam carving. J Inf Secur Appl 35:13–22Google Scholar
- 30.Zhang D, Li Q, Yang G, Li L, Sun X (2017) Detection of image seam carving by using weber local descriptor and local binary patterns. J Inf Secur Appl 36:135–144Google Scholar