Total views : 491

RST Invariant Image Forgery Detection


  • Department of ECE, JNTUK University College of Engineering, Kakinada - 533003, Andhra Pradesh, India
  • Department of ECE, JNTUK University College of Engineering, Vizianagaram - 535003, Andhra Pradesh, India


Background/Objectives: Copy-Move Forgery Detection (CMFD) is a very prevalent approach used to detect copy and pasted portions of the same image. The copied portion is rotated, flipped or scaled. The detection method should be invariant to rotation, scaling and translation. Many CMFD methods came into existence; however, some methods fail to withstand attacks such as Contrast adjustment, Gaussian blur and JPEG Compression. Although the methods are able to resist the attacks, they are computationally complex. This paper proposes a Rotation, Scaling, Translation (RST) invariant image forgery detection. Methods: Local Binary Pattern (LBP) is applied on the low frequency content of Discrete Wavelet Transform (DWT) decomposed image for feature extraction. Findings: The proposed method is invariant to rotation, scaling and translation attacks on the pasted portions of the image and able to resist post-processing attacks and has low computational effort. It is evaluated qualitatively and quantitatively on a CASIA database. Morphological operations are performed to reduce the false alarms. The correct detection ratio is in the range of 80% to 99% and false detection ratio in the range of 7% to 30%. Applications: There is a great demand to detect the forgery, which aids in the digital forensic analysis, in legal document substantiation, and various other fields.


Computational Complexity, Discrete Wavelet Transform, Image Forgery, Local Binary Pattern, Localization.

Full Text:

 |  (PDF views: 375)


  • Mahdian B, Stanislav S. A bibliography on blind methods for identifying image forgery. Signal Processing: Image Communication. 2010; 25(6):389–99.
  • Zhang J, Feng Z, Su Y. A new approach for detecting copy-move forgery in digital images. 11th Proceedings IEEE Singapore International Conference on Communication Systems, Guangzhou; 2008 Nov. p. 362–66.
  • Shivakumar BL, Baboo SS. Detecting copy-move forgery in digital images: a survey and analysis of current methods. Global Journal of Computer Science and Technology. 2010; 10(7):61–5.
  • Khan S, Kulakarni A. A reduced time complexity for detection of copy-move forgery detection using discrete wavelet transform. International of Computer Applications. 2010; 6(7):31–6.
  • Fridrich J, Soukal D, Lukas J. Detection of copy-move forgery in digital images. Proceedings Digital Forensic Research Workshop, Cleveland, OH, USA; 2003 Aug. p. 1–10.
  • Popescu AC, Farid H. Exposing digital forgeries by detecting duplicated image regions, Darmouth College: USA; 2004.
  • Li G, Wu Q, Tu D, Sun S. A sorted neighbourhood approach for detecting duplicated regions in image forgeries based on DWT and SVD. Proceedings IEEE International Conference on Multimedia and Expo, Beijing, China; 2007 Jul 2–5. p. 1750–53.
  • Yang B, Sun X, Chen X, Zhang J, Li X. An efficient forensic method for copy-move forgery detection based on DWT-FWHT. Radio Engineering. 2013; 22(4):1098–105.
  • Huang Y, Lu W, Sun W, Long D. Improved DCT-based detection of copy-move forgery in images. Forensic Science International. 2011; 206 (1–3):178–84.
  • Muhammad G, Hussain M, Khawani K, Bebis G. Blind copy move forgery detection using dyadic undecimated wavelet transform. Proceedings IEEE 17th International Conference on Digital Signal Processing, (DSP), Confu; 2011 Jul 6–8. p. 1–6.
  • Hashmi MF, Hambarde AR, Keskar AG. Copy move forgery detection using DWT and SIFT features. 13th Proceedings IEEE International Conference on Intelligent Systems Design and Applications, Bangi. 2013 Dec. p. 188–93.
  • Ghorbani M, Firouzmand M, Faraahi A. DWT-DCT (QCD) based copy-move image forgery detection. Proceedings 18th International Conference on Systems, Signals and Image Processing, Sarajevo; 2011 Jun. p. 1–4.
  • Mahadian B, Saic S. Detection of copy-move forgery using a method based on blur moment invariants. Forensic Science International. 2007; 171(2–3):180–89.
  • Ryu SJ, Lee MJ, Lee HK. Detection of copy-rotate-move forgery using Zernike Moments. Proceedings 12th International Conference on Information Hiding; 2010. p. 51–65.
  • Liu GJ, Wang JW, Lian SG, Wang ZQ. A passive image authentication scheme for detecting region-duplication forgery with rotation. Journal of Network and Computer Applications. 2011; 34(5):1557–65.
  • Li L, Li S, Zhu H. An efficient scheme for detecting copy-move forged images by Local Binary Patterns. Journal of Information Hiding and Multimedia Signal Processing. 2013; 4(1):46–56.
  • Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002; 24(7):971–87.
  • Li Z, Liu GZ, Yang Y, You JY. Scale and rotation-invariant local binary pattern using scale-adaptive texton sub uniform based circular shift. IEEE Transactions on Image Processing. 2012; 21(4):2130–40.
  • CASIA. Image tampering detection evaluation database [Internet]. [Cited 2014 Sep 17]. Available from:
  • Li L, Li S, Zhu H, Wu X. Detetcing copy-move forgery under affine transforms for image forensics. Computers and Electrical Engineering. 2014; 40(6):1951–62.
  • Muhammad G, Al-Hammadi MH, Hussain M, Bebis G. Image forgery detection using steerable pyramid transform and local binary pattern. Machine Vision and Applications. 2014; 25(4):985–95.
  • Rao CS, Babu SBGT. Image authentication using local binary pattern on the low frequency components. Microelectronics, Electromagnetics and Telecommunications. 2016; 372:529–37
  • Deepa M, Saravanan T. Automatic image registration using 2d-discrete wavelet transform. Indian Journal of Science and Technology. 2016 Feb; 9(5):1–3. Doi no:10.17485/ijst/2016/v9i5/58101.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.