An Analysis of Image Forgery Detection Techniques

Chandan deep Kaur, Navdeep Kanwal

Abstract


Society is becoming increasingly dependent on the internet and so does it become more and more vulnerable to harmful threats. These threats are becoming vigorous and continuously evolving. These threats distorted the authenticity of data transmitted through the internet. As we all completely or partially rely upon this transmitted data. Hence, its authenticity needs to be preserved. Images have the potential of conveying much more information as compared to the textual content. We pretty much believe everything that we see. In order to preserve/check the authenticity of images, image forgery detection techniques are expanding its domain. Detection of forgeries in digital images is in great need in order to recover the people’s trust in visual media. This paper is going to discuss different types of image forgery and blind methods for image forgery detection. It provides the comparative tables of various types of techniques to detect image forgery. It also gives an overview of different datasets used in various approaches of forgery detection.

Keywords


Image Forgery, Forgery Detection, Fake Pictures, Blind Methods

References


M. Ali and M. Deriche, “Signal Processing : Image Communication A bibliography of pixel-based blind image forgery detection techniques,” vol. 39, pp. 46–74, 2015.

J. Fridrich, D. Soukal, and J. Lukáš, “Detection of Copy-Move Forgery in Digital Images,” Int. J., vol. 3, no. 2, pp. 652–663, 2003.

G. K. Birajdar and V. H. Mankar, “Digital image forgery detection using passive techniques: A survey,” Digit. Investig., vol. 10, no. 3,pp. 226–245, 2013.

A. Kashyap, R. S. Parmar, M. Agrawal, and H. Gupta, “An Evaluation of Digital Image Forgery Detection Approaches,” 2017.

K. Sreenivas and V. Kamkshi Prasad, “Fragile watermarking schemes for image authentication: a survey,” Int. J. Mach. Learn. Cybern., vol. 0, no. 0, p. 0, 2017.

I. No, P. Kaur, and L. Clay, “Available Online at www.ijarcs.info,” vol. 8, no. 7, pp. 172–175, 2017.

M. Ali and M. Deriche, “Signal Processing : Image Communication A bibliography of pixel-based blind image forgery detection techniques,” 2015.

N. Kanwal, J. S. Bhullar, L. Kaur, and A. Girdhar, “A Taxonomoy and Analysis of Digital Image Forgery Detection Techniques,” vol. 10, no. I, pp. 35–41, 2017.

Z. Zhang, C. Wang, and X. Zhou, “A survey on passive image copy-move forgery detection,” J. Inf. Process. Syst., vol. 14, no. 1, pp. 6–31, 2018.

D. Chauhan, D. Kasat, S. Jain, and V. Thakare, “Survey on Keypoint Based Copy-move Forgery Detection Methods on Image,” Procedia Comput. Sci., vol. 85, no. Cms, pp. 206–212, 2016.

A. D. Warbhe, R. V. Dharaskar, and V. M. Thakare, “A Survey on Keypoint Based Copy-paste Forgery Detection Techniques,” Phys. Procedia, vol. 78, no. December 2015, pp. 61–67, 2016.

O. M. Al-Qershi and B. E. Khoo, “Passive detection of copy-move forgery in digital images: State-of-the-art,” Forensic Sci. Int., vol. 231, no. 1–3, pp. 284–295, 2013.

A. Doegar, “A Review Paper on Digital Image Forgery Detection Techniques,” IEEE Access, vol. 1, no. 3, pp. 1–5, 2015.

O. M. Al-Qershi and B. E. Khoo, “Comparison of Matching Methods for Copy-Move Image Forgery Detection,” in 9th International Conference on Robotic, Vision, Signal Processing and Power Applications, 2017, pp. 209–218.

B. Soni, P. K. Das, and D. M. Thounaojam, “CMFD: a detailed review of block based and key feature based techniques in image copy-move forgery detection,” IET Image Process., vol. 12, no. 2, pp. 167–178, 2018.

Y. Huang, W. Lu, W. Sun, and D. Long, “Improved DCT-based detection of copy-move forgery in images,” Forensic Sci. Int., vol. 206, no. 1–3, pp. 178–184, 2011.

S. Khan and A. Kulkarni, “An Efficient Method for Detection of Copy-Move Forgery Using Discrete Wavelet Transform,” Int. J., vol. 2, no. 5, pp. 1801–1806, 2010.

M. K. Bashar, K. Noda, N. Ohnishi, H. Kudo, T. Matsumoto, and Y. Takeuchi, “Wavelet-Based Multiresolution Features for Detecting Duplications in Images,” Mach. Vis. Appl., pp. 2–5, 2007.

G. Muhammad, M. Hussain, and G. Bebis, “Passive copy move image forgery detection using undecimated dyadic wavelet transform,” Digit. Investig., vol. 9, no. 1, pp. 49–57, 2012.

Bayram, “AN EFFICIENT AND ROBUST METHOD FOR DETECTING COPY-MOVE FORGERY Sevinc Bayram Polytechnic Institute of NYU ECE Dept . Brooklyn , NY Husrev Taha Sencar TOBB University of Economics & Technology Ankara , TURKEY Nasir Memon Polytechnic Insitute of NYU CIS Dep,” Image (Rochester, N.Y.), pp. 1053–1056, 2009.

Y. Li, “Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching,” Forensic Sci. Int., vol. 224, no. 1–3, pp. 59–67, 2013.

A. C. Popescu and H. Farid, “Exposing digital forgeries by detecting duplicated image regions,” Dept. Comput. Sci., Dartmouth Coll. Tech. Rep. TR2004-515, no. 2000, pp. 1–11, 2004.

M. Bashar, K. Noda, N. Ohnishi, and K. Mori, “Exploring duplicated regions in natural images,” IEEE Trans. Image Process., no. 99, pp. 1–40, 2010.

Z. Ting and W. Rang-Ding, “Copy-move forgery detection based on SVD in digital image,” Proc. 2009 2nd Int. Congr. Image Signal Process. CISP’09, no. 2, pp. 0–4, 2009.

H. C. Hsu and M. S. Wang, “Detection of copy-move forgery image using Gabor descriptor,” Proc. Int. Conf. Anti-Counterfeiting, Secur. Identification, ASID, pp. 1–4, 2012.

D. G. Lowe, “Object recognition from local scale-invariant features,” Proc. Seventh IEEE Int. Conf. Comput. Vis., pp. 1150–1157 vol.2, 1999.

H. Hailing, G. Weiqiang, and Z. Yu, “Detection of copy-move forgery in digital images using sift algorithm,” Proc. - 2008 Pacific-Asia Work. Comput. Intell. Ind. Appl. PACIIA 2008, vol. 2, pp. 272–276, 2008.

I. Amerini, L. Ballan, R. Caldelli, A. Del Bimbo, and G. Serra, “A SIFT-based forensic method for copy-move attack detection and transformation recovery,” IEEE Trans. Inf. Forensics Secur., vol. 6, no. 3 PART 2, pp. 1099–1110, 2011.

X. Pan and S. Lyu, “Detecting image region duplication using sift features,” ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. Proc., pp. 1706–1709, 2010.

X. Pan, S. Member, and S. L. Member, “Region Duplication Detection Using Image Feature Matching,” vol. X, no. X, pp. 1–11, 2010.

B. L. Shivakumar and S. S. Baboo, “Detection of Region Duplication Forgery in Digital Images Using SURF.,”Int. J. Comput. Sci. Issues, vol. 8, no. 4, pp. 199–205, 2011.

B. Xu, J. Wang, G. Liu, and Y. Dai, “Image copy-move forgery detection based on SURF,” Proc. - 2010 2nd Int. Conf. Multimed. Inf. Netw. Secur. MINES 2010, pp. 889–892, 2010.

M. Jaberi, G. Bebis, M. Hussain, and G. Muhammad, “Accurate and robust localization of duplicated region in copy-move image forgery,” Mach. Vis. Appl., vol. 25, no. 2, pp. 451–475, 2014.

G. Liu, J. Wang, S. Lian, and Z. Wang, “A passive image authentication scheme for detecting region-duplication forgery with rotation,” J. Netw. Comput. Appl., vol. 34, no. 5, pp. 1557–1565,2011.

S. J. Ryu, M. Kirchner, M. J. Lee, and H. K. Lee, “Rotation invariant localization of duplicated image regions based on zernike moments,” IEEE Trans. Inf. Forensics Secur., vol. 8, no. 8, pp. 1355-1370, 2013.

B. Mahdian and S. Saic, “Detection of copy-move forgery using a method based on blur moment invariants,” Forensic Sci. Int., vol. 171, no. 2–3, pp. 180–189, 2007.

W. Luo and J. Huang, “Robust Detection of Region-Duplication Forgery in Digital Image,” pp. 18–21, 2006.

E. Ardizzone, A. Bruno, and G. Mazzola, “Copy-move forgery detection via texture description,” Proc. 2nd ACM Work. Multimed. forensics, Secur. Intell. - MiFor ’10, p. 59, 2010.

G. Li, Q. Wu, D. Tu, and S. Sun, “A Sorted Neighborhood Approach for Detecting Duplicated Regions in Image Forgeries Based on DWT and SVD,” Multimed. Expo, 2007 IEEE Int. Conf., pp. 1750 1753, 2007.

M. Ghorbani, M. Firouzmand, and A. Faraahi, “DWT-DCT ( QCD ) Based Copy-move Image Forgery Detection,” no. April, 2011.

Z. Mohamadian, “Image Duplication Forgery Detection using Two Robust Features,” Res. J. Recent Sci., vol. 1, no. 12, pp. 1–6, 2012.

L. Li, S. Li, H. Zhu, S.-C. Chu, J. F. Roddick, and J.-S. Pan, “An efficient scheme for detecting copy-move forged images by local binary patterns,” J. Inf. Hiding Multimed. Signal Process., vol. 4, no. 1, pp. 46–56, 2013.

S. M. Thampi, S. Bandyopadhyay, S. Krishnan, K. C. Li, S. Mosin, and M. Ma, “Advances in signal processing and intelligent recognition systems: Proceedings of Second International Symposium on Signal Processing and Intelligent Recognition Systems (SIRS-2015), December 16–19, 2015, Trivandrum, India,” Adv. Intell. Syst.Comput., vol. 425, pp. 645–654, 2016.

F. Yang, J. Li, W. Lu, and J. Weng, “Copy-move forgery detection based on hybrid features,” Eng. Appl. Artif. Intell., vol. 59, no. December 2016, pp. 73–83, 2017.

B. B. M. P. N and A. K. M, “Copy-Move Forgery Detection Using Segmentation,” 2017 11th Int. Conf. Intell. Syst. Control, pp. 224-228, 2017.

M. Emam, Q. Han, Q. Li, and H. Zhang, “A robust detection algorithm for image Copy-Move forgery in smooth regions,” 2017 Int. Conf. Circuits, Syst. Simulation, ICCSS 2017, no. c, pp. 119–123, 2017.

C. Chou and J. Lee, “Copy-Move Forgery Detection Based on Local Gabor Wavelets Patterns,” vol. 2, pp. 47–56, 2018.

J. Dong, W. Wang, T. Tan, and Y. Q. Shi, “Run-length and edge statistics based approach for image splicing detection,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 5450 LNCS, pp. 76–87, 2009.

Z. He, W. Sun, W. Lu, and H. Lu, “Digital image splicing detection based on approximate run length,” Pattern Recognit. Lett., vol. 32, no. 12, pp. 1591–1597, 2011.

Z. He, W. Lu, W. Sun, and J. Huang, “Digital image splicing detection based on Markov features in DCT and DWT domain,” Pattern Recognit., vol. 45, no. 12, pp. 4292–4299, 2012.

W. Chen, Y. Q. Shi, C. Chen, and W. Chen, “A Natural Image Model Approach to Splicing Detection A Natural Image Model Approach to Splicing Detection,” no. April 2014, 2007.

A. A. Alahmadi, M. Hussain, H. Aboalsamh, G. Muhammad, G. Bebis, and Ieee, “Splicing Image Forgery Detection Based on DCT and Local Binary Pattern,” 2013 Ieee Glob. Conf. Signal Inf. Process., pp. 253–256, 2013.

X. Li, T. Jing, and X. Li, “Image splicing detection based on moment features and Hilbert-Huang Transform,” Proc. 2010 IEEE Int. Conf. Inf. Theory Inf. Secur. ICITIS 2010, pp. 1127–1130, 2010.

I. Cox, Digital Watermarking, vol. 11, no. 3. 2002.

J. Zhang, Y. Zhao, and Y. Su, “A new approach merging Markov and DCT features for image splicing detection,” Proc. - 2009 IEEE Int. Conf. Intell. Comput. Intell. Syst. ICIS 2009, vol. 4, no. 9, pp. 390-394, 2009.

L. B. Pattern, “Splicing Image Forgery Detection Based on DCT and Local Binary Pattern,” pp. 253–256, 2013.

Z. Moghaddasi, H. A. Jalab, R. Noor, and S. Aghabozorgi, “Improving RLRN Image Splicing Detection with the Use of PCA and Kernel PCA,” vol. 2014, 2014.

E. S. M. El-Alfy and M. A. Qureshi, “Combining spatial and DCT based Markov features for enhanced blind detection of image splicing,” Pattern Anal. Appl., vol. 18, no. 3, pp. 713–723, 2015.

C. Chen and S. Mccloskey, “Image Splicing Detection via Camera Response Function Analysis Supplementary Material,” pp. 2–4.

M. M. Isaac and M. Wilscy, “Multiscale Local Gabor Phase Quantization for image forgery detection,” Multimed. Tools Appl., vol. 76, no. 24, pp. 25851–25872, 2017.

E. Kee and H. Farid, “A perceptual metric for photo retouching,” Proc. Natl. Acad. Sci., vol. 108, no. 50, pp. 19907–19912, 2011.

D. T. Trung, A. Beghdadi, and M. C. Larabi, “Blind inpainting forgery detection,” 2014 IEEE Glob. Conf. Signal Inf. Process. Glob. 2014, pp. 1019–1023, 2014.

H. Gunes and M. Piccardi, “Assessing facial beauty through proportion analysis by image processing and supervised learning,” Int. J. Hum. Comput. Stud., vol. 64, no. 12, pp. 1184–1199, 2006.

a Dantcheva and J. Dugelay, “Female facial aesthetics based on soft biometrics and photo-quality,” IEEE Conf. Inst. Comput. Math. Eng., 2011.

M. . D.Xie,L.Liang,L.Jin,J.Xu, “A benchmark dataset for facial beauty perception,” http://www.hcii-lab.net/data/SCUT-FBP.

D. Zhang, Z. Liang, G. Yang, Q. Li, L. Li, and X. Sun, “A robust forgery detection algorithm for object removal by exemplar-based image inpainting,” Multimed. Tools Appl., vol. 30, pp. 1–20, 2017.

Y. Q. Zhao, M. Liao, F. Y. Shih, and Y. Q. Shi, “Tampered region detection of inpainting JPEG images,” Optik (Stuttg)., vol. 124, no. 16, pp. 2487–2492, 2013.

I. Amerini, L. Ballan, R. Caldelli, A. Del Bimbo, L. Del Tongo, and G. Serra, “Copy-move forgery detection and localization by means of robust clustering with J-Linkage,” Signal Process. Image Commun.,vol. 28, no. 6, pp. 659–669, 2013.

I. Amerini, R. Becarelli, R. Caldelli, and A. Del Mastio, “Splicing forgeries localization through the use of first digit features,” 2014 IEEE Int. Work. Inf. Forensics Secur. WIFS 2014, pp. 143–148, 2015.

T.-T. Ng and S. Chang, “A Data Set of Authentic and Spliced Image Blocks,” Columbia Univ. Tech. Rep. 203-2004-3, pp. 1–9, 2004.

S.-F. C. Jessie Hsu, Tian-Tsong Ng, “Columbia Image Splicing Detection Evaluation Dataset.” [Online]. Available: http://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/AuthSplicedDataSet.htm. [Accessed: 10-May-2018].

Y. F. Hsu and S. F. Chang, “Detecting image splicing using geometry invariants and camera characteristics consistency,” 2006 IEEE Int. Conf. Multimed. Expo, ICME 2006 - Proc., vol. 2006, pp.549–552, 2006.


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DOI: 10.19139/soic.v7i2.542

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