Latest Trends in Image Forgery Detection
DOI:
https://doi.org/10.26438/ijcse/v7i12.4145Keywords:
Image Forgery, Block based, Key point basedAbstract
Digital image forensic is a part of multimedia security with the objective to expose the image forgery in digital images. Among different types of image forgeries available, copy–move forgery is the most popular and common forgery. In Copy-move forgery one part of the original digital image is copied and pasted at any other position in the same image. Several methods have been developed to detect the image forgery in digital images. This paper is focusing on pixel-based copy–move image forgery detection methods to detect forgery which later on includes the trending algorithms of Key point based techniques and Block based techniques. Various techniques have been mentioned in the paper from the literature which was used by different authors for feature extraction and forgery detection. Comparative study of key point and block based image forgery detection algorithms is also stated.
References
[1] A. Kashyap, R. S. Parmar, M. Agarwal, and H. Gupta, “An evaluation of digital image forgery detection approaches,” Int. J. Appl. Eng. Res., vol. 12, no. 15, pp. 4747–4758, 2017.
[2] M. Ismail and N. Kanwal, “a Review Block Based Copy Move Forgery Detection Techniques,” Int. J. Comput. Sci. Mob. Comput., vol. 7, no. 4, pp. 205–212, 2018.
[3] X. Cun and C. M. Pun, “Image splicing localization via semi-global network and fully connected conditional random fields,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11130 LNCS, pp. 252–266, 2019.
[4] V. Christlein, C. Riess, J. Jordan, C. Riess, and E. Angelopoulou, “An evaluation of popular copy-move forgery detection approaches,” IEEE Trans. Inf. Forensics Secur., vol. 7, no. 6, pp. 1841–1854, 2012.
[5] M. hak and T. Gulati, “Detection of Digital Forgery Image using Different Techniques,” Int. J. Eng. Trends Technol., vol. 46, no. 8, pp. 457–461, 2017.
[6] S. Mushtaq and A. H. Mir, “Image Copy Move Forgery Detection: A Review,” Int. J. Futur. Gener. Commun. Netw., vol. 11, no. 2, pp. 11–22, 2018.
[7] S. Walia and K. Kumar, “Digital image forgery detection: a systematic scrutiny,” Aust. J. Forensic Sci., vol. 51, no. 5, pp. 488–526, 2019.
[8] S. Sadeghi, S. Dadkhah, H. A. Jalab, G. Mazzola, and D. Uliyan, “State of the art in passive digital image forgery detection: copy-move image forgery,” Pattern Anal. Appl., vol. 21, no. 2, pp. 291–306, 2018.
[9] E. Isha and E. V. Goyal, “A literature review of Image Forgery Detection,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 4, no. IX, pp. 75–80, 2016.
[10] T. Dalal, “Survey of Image Forgery Detection Technique Based on Color Illumination Using Machine Learning Approach,” Int. J. Adv. Res. Ideas Innov. Technol., vol. 2, no. 3, pp. 1–7, 2016.
[11] 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.
[12] S. Panda and M. Mishra, “Passive techniques of digital image forgery detection: Developments and challenges,” Lect. Notes Electr. Eng., vol. 443, pp. 281–290, 2018.
[13] A. Sahay and A. Gautam, “Comparison between SIFT and SURF image forgery Algorithms,” Int. J. Comput. Appl., vol. 164, no. 2, pp. 9–11, 2017.
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