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Exposing Image Manipulation with Curved Surface Reflection


  • Department of CSE, St. Peter’s University, Chennai - 600054, Tamil Nadu, India
  • Dr. M.G.R University, Chennai - 600032, Tamil Nadu, India


Objectives: One of the principal problem in image forensics is identifying if a particular image is manipulated or not. Nowadays powerful image editing software increases the difficulty in finding the authentic image. Our approach is to detect curved surface manipulated area inserted in the photograph by describing a geometrical technique. Methods/ Statistical Analysis: This technique is combined with multiple image segmentation methodologies. The main objective of our work is to identify the manipulated area from the reflected images. Findings: Our contribution of this paper is to design a new geometrical relationship between the original and fake images. We use the new notations for this geometrical representation and combine these notations to the basic image segmentation methods. Finally we identify the fake reflected image using the different approaches and compare the results and produce the best performance of our work. Application/Improvements: The experimental result shows that our method achieves promising performance for tampering the curved surface reflections. The result of our work, we compared the 100 images and produce the best PSNR value and compression ratio.


Curved Surface, Forgery Detection, Image Forensics, Reflection, Top-Hat.

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  • Kee E, Brien JFO, Farid H. Exposing photo manipulation from shading and shadows. ACM Transaction on raGphics. 2014.
  • Amerini I, Ballan L, Caldelli R, Bimb AD, Serra G. A SIFT-based forensic method for copy-move attack detection and transformation recovery. IEEE Trans Inf Forensic Security. 2011; 16(3):1099–109.
  • Ng TT, Chang SF, Sun Q. A data set of authentic and spliced image blocks Tech Rep., DVMM, Columbia University, Dataset. 2004.
  • Jambhekar ND, Dhawale CA. Bit Level Key Agreement and Exchange Protocol for Digital Image Steganography. Indian Journal of Science and Technology. 2015 Jul; 8(15):1–7.
  • Devi Mahalakshmi S, Vijayalakshmi K, Priyadharshini S. Digital image forgery detection and estimation by exploring basic image manipulation. Digital investigation. 2012; 8:215–25.
  • Carvalho TJD, Riess C, Angelopoulou E, Pedrini H, Rocha ADR. Exposing digital image forgeries by illumination color classification. IEEE Trans Inf Forensics Security. 2013; 8(7):11–82.
  • Kee E, James FO, Brien B, Farid H. Photo manipulation with inconsistent shadows. ACM transaction on graphics. 2013; 32(4):1–12.
  • Binachi T, Piva A. Image Forgery localization Via Block Grained Analysis of JPEG Artifacts. IEEE Trans Inf Forensics Security. 2012; 7(3):1003–15.
  • James FO, Brein B, Farid H. Exposing Photo Manipulation with Inconsistent Reflection. ACM Transations on Graphics, California. 2012; 1–11.
  • Wang W, Dong J, Tan T. A Survey of Passive Image Tampering Detection. Proceedings of the 8th International Workshop Digital Watermarking. Springer-Verlag. 2009; 308–22.
  • Sharma KKM. A Method for Binary Image Thinning using Gradient and Watershed Algorithm. International Journal of Advanced Research in Computer Science and Software Engineering. 2013; 3(1):1–4.
  • Ye S, Sun Q, Chang E. Detecting digital image forgeries based on demos icing artefact. IEEE International Conference on Multimedia and Expo, USA. 2009. p. 12–5.
  • Hazem A, AI-Otum M. Color Image Authentication for Detecting Intentional Attacks. IEEE, Jordan. 2016. ISBN: 978-1-4673-7504-7 ©
  • Carvalho T, Fabio A, Faria F, Pedrini H. Illuminant-based Transformed Spaces for Image Forensics. IEEE Transactions on Information Forensics and Security. 2016; 11(4):720–33.
  • Vaishnavi D, Subashini TS. Recognizing Image Splicing Forgeries using Histogram Features. 3rd MEC International Conference on Big Data and Smart City, IEEE. India. 2016.
  • Zandi M, Mahmoudi-Aznaveh A, Talebpour A. Iterative copy-move forgery detection based on a new interest point detector. IEEE Transactions on Information Forensics and Security. 2016; 11(11):2499–512.
  • Aymaz S, Aymaz S, Uluta G. Detection of Copy Move Forgery using Legendre Moments. 24th Signal Processing and Communication Application Conference, IEEE, Turkey. 2016. p. 1–4.
  • Wang W, Dong J, Tan T. Effective image splicing detection based on image chroma. IEEE International Conference on Image Processing, China. 2009. p. 1257–60.
  • Kee E, James FO, Brien B, Farid H. Exposing photo manipulation from shading and shadows. ACM transaction on graphics, USA. 2014; 33(5):165.
  • Popescu AC, Farid H. Exposing digital forgeries in color filter array interpolated images. IEEE Transactions on Signal Processing. 2005; 53(10):3948–59.
  • Swain G. Digital image steganography using nine-pixel differencing and modified lsb substitution. Indian Journal of Science and Technology. 2014 Jan; 7(9):1–7.
  • Cheol-Joo C, Kiseok C, Kwang-Nam C. The XML based Electronic Document Image Retrieval System. Indian Journal of Science and Technology. 2015 May; 8(S9):1–5.
  • Kadali KS, Rajaji L. FPGA and ASIC implementation of systolic arrays for the design of optimized median filter in digital image processing applications. Indian Journal of Science and Technology. 2014 Nov; 7(S7):1–5.
  • Johnson MK, Farid H. Exposing digital forgeries by detecting inconsistencies in lighting in ACM Multimedia and Security Workshop, USA. 2005; 1–10.
  • Wang W, Dong J, Tan T. Image tampering detection based on stationary distribution of markov chain In: IEEE International Conference on Image Processing, China. 2010.
  • Dararzani R, Yaghmaie K, Mozaffari S, Tapak M. Copy-Move forgery detection using multi resolution local binary patterns Forensic Sci Int. 2013; 231(1-3):61–72.
  • Al-amri1 SS, Kalyankar NV, Khamitkar KSD. Image Segmentation by Using Thershod Techniques. Journal of Computing. 2010; 2(5):1–8.


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