Skip to main content

Advertisement

Log in

A comprehensive survey of image and video forgery techniques: variants, challenges, and future directions

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

With the advent of Internet, images and videos are the most vulnerable media that can be exploited by criminals to manipulate for hiding the evidence of the crime. This is now easier with the advent of powerful and easily available manipulation tools over the Internet and thus poses a huge threat to the authenticity of images and videos. There is no guarantee that the evidences in the form of images and videos are from an authentic source and also without manipulation and hence cannot be considered as strong evidence in the court of law. Also, it is difficult to detect such forgeries with the conventional forgery detection tools. Although many researchers have proposed advance forensic tools, to detect forgeries done using various manipulation tools, there has always been a race between researchers to develop more efficient forgery detection tools and the forgers to come up with more powerful manipulation techniques. Thus, it is a challenging task for researchers to develop h a generic tool to detect different types of forgeries efficiently. This paper provides the detailed, comprehensive and systematic survey of current trends in the field of image and video forensics, the applications of image/video forensics and the existing datasets. With an in-depth literature review and comparative study, the survey also provides the future directions for researchers, pointing out the challenges in the field of image and video forensics, which are the focus of attention in the future, thus providing ideas for researchers to conduct future research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. Farid, H.: Digital doctoring: how to tell the real from the fake. Significance 3(4), 162–166 (2006)

    Article  MathSciNet  Google Scholar 

  2. Zhu, B.B., Swanson, M.D., Tewfik, A.H.: When seeing isn’t believing. IEEE Signal Process. Mag. 21(2), 40–49 (2004)

    Article  Google Scholar 

  3. “Photo tampering throughout history,” (2012). http://www.fourandsix.com/photo-tampering-history/

  4. Redi, J.A., Taktak, W., Dugelay, J.-L.: Digital image forensics: a booklet for beginners. Multimed. Tools ppl. 51(1), 133–162 (2010)

    Article  Google Scholar 

  5. Parveen, A., Tayal, A.: An algorithm to detect the forged part in an image. In: Proceedings of 2nd International Conference on Communication and Signal Processing, 1486–1490 (2016)

  6. Yan, C., Li, Z., Zhang, Y., Liu, Y., Ji, X., Zhang, Y.: Depth Image denoising using nuclear norm and learning graph model. ACM Trans. Multimed. Comput. Commun. Appl. 16(4), 1–17 (2021)

    Article  Google Scholar 

  7. Zheng, H., Yong, H., Zhang, L. Deep convolutional dictionary learning for image de-noising. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, 630–641 (2021)

  8. Shi, Q., Tang, X., Yang, T., Liu, R., Zhang, L.: Hyperspectral image de-noising using a 3-D attention denoising network. IEEE Trans. Geosci. Remote Sens., pp. 1–16 (2021)

  9. Yan, C., Hao, Y., Li, L., Yin, J., Liu, A., Mao, Z., Gao, X.: Task-adaptive attention for image captioning. IEEE Trans. Circ. Syst. Video Technol., 1–1 (2021)

  10. Quan, Y., Chen, Y., Shao, Y., Teng, H., Xu, Y., Ji, H.: Image de-noising using complex-valued Deep CNN. Pattern Recognit. 111, 107639 (2020)

    Article  Google Scholar 

  11. Lan, R., Zou, H., Pang, C., Zhong, Y., Liu, Z., Luoet, X.: Image denoising via deep residual convolutional neural networks. SIViP 15, 1–8 (2021)

    Article  Google Scholar 

  12. Cheng, S., Wang, Y., Huang, H., Liu, D., Fan, H., Liu, S.: NBNet: Noise basis learning for image de-noising with subspace projection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4896–4906 (2021)

  13. Jaseela, S., Nishadha, S.G.: Survey on copy move image forgery detection techniques. Int. J. Comput. Sci. Trends Technol. (IJCST) 4(1), 87–91 (2016)

    Google Scholar 

  14. Fadl, S.M., Semary, N.O.A., Hadhoud, M.M.: Copy–rotate–move forgery detection based on spatial domain. In: Proceedings of 9th International Conference on Computer Engineering and Systems, pp. 136–141 (2014)

  15. Ren, X.: An optimal image thresholding using genetic algorithm. Int. Forum Comput. Sci.-Technol. Appl. 1, 169–172 (2009)

    Google Scholar 

  16. Hussain, M., Muhammad, G., Saleh, S.Q., Mirza, A.M., Bebis, G.: Copy–move image Forgery detection using multi-resolution weber descriptors. In: Proceedings of 8th International Conference on Signal Image Technology and Internet Based Systems, pp. 1570–1577 (2013)

  17. Agarwal, V., Mane, V.: Reflective SIFT for improving the detection of copy–move image forgery. In: Proceedings of 2nd International Conference on Research in Computational Intelligence and Communication Networks, pp. 84–88 (2016)

  18. Amerini, I., Ballan, L., Caldelli, R., Bimbo, A.D., Serra, G.: A SIFT-Based forensic method for copy–move attack detection and transformation recovery. IEEE Trans. Inf. Forensics Secur. 6(3), 1099–1110 (2011)

    Article  Google Scholar 

  19. He, Z., Lu, W., Sun, W., Huang, J.: Digital image splicing detection based on markov features in DCT and DWT domain. Pattern Recogn. 45(12), 4292–4299 (2012)

    Article  Google Scholar 

  20. Shahroudnejad, A., Rahmati, M.: Copy–move forgery detection in digital images using affine-SIFT. In: Proceedings of 2nd International Conference of Signal Processing and Intelligent Systems, pp. 1–5 (2016)

  21. Lin, S.D., Wu, T.: An integrated technique for splicing and copy–move forgery image detection. In: Proceedings of 4th International Conference on Image and Signal Processing, 2:1086–1091 (2011)

  22. Ting, Z., Rang-ding, W.: Copy–move forgery detection based on SVD in digital image. In: Proceedings of 2nd International Conference on Image and Signal Processing, 1–5 (2009)

  23. Koppanati, R.K., Kumar, K.: P-MEC: polynomial congruence-based multimedia encryption technique over cloud. IEEE Consum. Electron. Mag. 10(5), 41–46 (2021)

    Article  Google Scholar 

  24. Yan, C., Gong, B., Wei, Y., Gao, Y.: Deep multi-view enhancement hashing for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 43, 1 (2020)

    Google Scholar 

  25. Chaudhuri, U., Banerjee, B., Bhattacharya, A.: Siamese graph convolutional network for content based remote sensing image retrieval. Comput. Vis. Image Underst. 184, 22–30 (2019)

    Article  Google Scholar 

  26. Tolias, G., Sicre, R., Jegou, H.: Particular object retrieval with ´ integral max-pooling of CNN activations. In: ICLR, pp. 1–12 (2015)

  27. Xu, J., Wang, C., Qi, C., Shi, C., Xiao, B.: Unsupervised part-based weighting aggregation of deep convolutional features for image retrieval. In: AAAI, 2018, 32(1), pp. 7436–7443 (2018)

  28. Liu, H., Wang, R., Shan, S., Chen, X.: Deep supervised hashing for fast image retrieval. In: CVPR, 2016, pp. 2064–2072 (2016)

  29. Yan, K., Wang, Y., Liang, D., Huang, T., Tian, Y.: CNN vs. SIFT for image retrieval: alternative or complementary? In: ACM MM, 2016, 407–411 (2016)

  30. Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., Pietikainen, M.: Deep learning for generic object detection: a survey. Int. J. Comput. Vis. 128(2), 261–318 (2020)

    Article  MATH  Google Scholar 

  31. Sridevi, M., Mala, C., Sandeep, S.: Copy–move image forgery detection in a parallel environment. In: Proceedings of CS & IT Computer Science Conference Proceedings (CSCP), pp. 19–29 (2012)

  32. Kang, L., Cheng, X.P.: Copy–move forgery detection in digital image. In: Proceedings of 3rd International Congress on Image and Signal Processing (CISP), vol. 5, pp. 2419–2421 (2010)

  33. Li, H., Luo, W., Qiu, X., Huang, J.: Image forgery localization via integrating tampering possibility maps. IEEE Trans. Inf. Forensics Secur. 12, 1–13 (2017)

    Article  Google Scholar 

  34. Al-Sanjary, O.I., Sulong, G.: Detection of video forgery: A review of literature. J. Theoret. Appl. Inf. Technol. 74(2), 217–218 (2015)

    Google Scholar 

  35. Ng, T., Chang, S.: A data set of authentic and spliced image blocks (2004)

  36. Hsu, Y., Chang, S.: Detecting image splicing using geometry invariants and camera characteristics consistency. In: 2006 IEEE International Conference on Multimedia and Expo, 549–552 (2006)

  37. Jegou, H., Douze, M., Schmid, C.: Hamming Embedding and Weak geometry consistency for large scale image search. In: Proceedings of the 10th European conference on Computer vision, October, 2008 (2008)

  38. Gloe, T., Bohme, R.: The dresden image database for benchmarking digital image forensics. J. Digital Forensic Pract. 3(2–4), 150–159 (2010)

    Article  Google Scholar 

  39. Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., Serra, G.: A SIFT-based forensic method for copy-move attack detection and transformation recovery. IEEE Trans. Inf. Forensics Secur. 6(3), 1099–1110 (2011)

    Article  Google Scholar 

  40. Bas, P., Filler, T., Pevny, T.: (2011). May Break our steganographic system: the ins and outs of organizing BOSS. In: International Workshop on Information Hiding, pp. 59–70 (2011)

  41. Bianchi, T., Piva, A.: Image forgery localization via block-grained analysis of JPEG artifacts. IEEE Trans. Inf. Forensics Secur. 7(3), 1003–1017 (2012)

    Article  Google Scholar 

  42. Christlein, V., Riess, C., Jordan, J., Riess, C., Angelopoulou, E.: An evaluation of popular copy-move forgery detection approaches. IEEE Trans. Inf. Forensics Secur. 7(6), 1841–1854 (2012)

    Article  Google Scholar 

  43. Tralic, D., Zupancic, I., Grgic, S., Grgic, M.: CoMoFoD—New database for copy–move forgery detection. In: International Symposium Electronics in Marine, pp. 49–54 (2013)

  44. Dong, J., Wang, W., Tan, T.: CASIA image tampering detection evaluation database. In: 2013 IEEE China Summit and International Conference on Signal and Information Processing (2013)

  45. Amerini, I., Ballan, L., Caldelli, R., Del-Bimbo, A., Del-Tongo, L., Serra, G.: Copy-move forgery detection and localization by means of robust clustering with J-Linkage. Signal Process. Image Commun. 28(6), 659–669 (2013)

    Article  Google Scholar 

  46. Cozzolino, D., Poggi, G., Verdoliva, L.: Copy-move forgery detection based on PatchMatch. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 5312–5316 (2014)

  47. Ardizzone, E., Bruno, A., Mazzola, G.: Copy-move forgery detection by matching triangles of keypoints. IEEE Trans. Inf. Forensics Secur. 10, 2084–2094 (2015)

    Article  Google Scholar 

  48. Dang-Nguyen, D.T., Pasquini, C., Conotter, V., Boato, G.: RAISE- A raw images dataset for digital image forensics. In: Proc. 6th ACM Multimed. Syst. Conf. MMSys 2015, pp. 219–224 (2015)

  49. Wattanachote, K., Shih, T.K., Chang, W.-L., Chang, H.-H.: Tamper detection of JPEG image due to seam modifications. IEEE Trans. Inf. Forensics Secur. 10(12), 2477–2491 (2015)

    Article  Google Scholar 

  50. Silva, E., Carvalho, T., Ferreira, A.: A. Rocha, going deeper into copy- move forgery detection: Exploring image telltales via multi-scale analysis and voting processes. J. Vis. Commun. Image Represent. 29, 16–32 (2015)

    Article  Google Scholar 

  51. Zampoglou, M., Papadopoulos, S., Kompatsiaris, Y.: Detecting image splicing in the wild (WEB). In: 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1–6 (2015)

  52. Wen, B., Zhu, Y., Subramanian, R., Ng, T.T., Shen, X., Winkler, S.: COVERAGE—a novel database for copy-move forgery detection. In: Proc. - Int. Conf. Image Process. ICIP.2016-August, 161–165 (2016)

  53. National Inst. of Standards and Technology (2016). The 2016 Nimble challenge evaluation dataset, https://www.nist.gov/itl/iad/mig/nimble-challenge, (2016)

  54. Korus, P., Huang, J.J.: Multi-scale analysis strategies in PRNU-based tampering localization. IEEE Trans. Inf. Forensics Secur. 12(4), 809–824 (2017)

    Article  Google Scholar 

  55. Guan, H., Kozak, M., Robertson, E., Lee, Y., Yates, A., Delgado, A., Zhou, D., Kheyrkhah, T., Smith, J., Fiscus, J.: MFC Datasets: Large-Scale Benchmark Datasets for Media Forensic Challenge Evaluation, IEEE Winter Conference on Applications of Computer Vision (WACV 2019), Waikola, HI, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=927035. (2019)

  56. Novozámský, A., Mahdian, B., Saic, S.: IMD2020: a large-scale annotated dataset tailored for detecting manipulated images. In: 2020 IEEE Winter Applications of Computer Vision Workshops (WACVW), pp. 71–80 (2020)

  57. Qadir, G., Yahahya, S., Ho, A.: Surrey University Library for Forensic Analysis (SULFA). In: Proceedings of the IETIPR 2012, 3–4 July, London (2012)

  58. Bestagini, P., Milani, S., Tagliasacchi, M., Tubaro, S.: Local tampering detection in video sequences. In: 2013 IEEE 15Th International Workshop on Multimedia Signal Processing (MMSP). IEEE, pp. 488–493 (2013)

  59. Al-Sanjary, O.I., Ahmed, A.A., Sulong, G.: Development of a video tampering dataset for forensic investigation. Forensic Sci. Int. 266, 565–572 (2016)

    Article  Google Scholar 

  60. Chen, S., Tan, S., Li, B., Huang, J.: Automatic detection of object-based forgery in advanced video. IEEE Trans. Circ. Syst. Video Tech. 26(11), 2138–2151 (2016)

    Article  Google Scholar 

  61. D’Avino, D., Cozzolino, D., Poggi, G., Verdoliva, L.: Autoencoder with recurrent neural networks for video forgery detection. Electron. Image 2017(7), 92–99 (2017)

    Article  Google Scholar 

  62. Ulutas, G., Ustubioglu, B., Ulutas, M., Nabiyev, V.V.: Frame duplication detection based on bow model. Multimed. Syst. 24(5), 549–567 (2018)

    Article  Google Scholar 

  63. D’Amiano, L., Cozzolino, D., Poggi, G., Verdoliva, L.: A patch match-based dense-field algorithm for video copy-move detection and localization. IEEE Trans. Circ. Syst. Video Technol. 29, 669–682 (2018)

    Article  Google Scholar 

  64. Panchal, H.D., Shah, H.B.: Video tampering dataset development in temporal domain for video forgery authentication. Multimed. Tools Appl. 79, 24553–24577 (2020)

    Article  Google Scholar 

  65. Ferreira, W.D., Ferreira, C.B., Junior, G.D., Soares, F.: A review of digital image forensics. Comput. Electr. Eng. 85, 106685 (2020)

    Article  Google Scholar 

  66. Birajdar, G.K., Mankar, V.H.: Digital image forgery detection using passive techniques: a survey. Digit. Investig. 10(3), 226–245 (2013)

    Article  Google Scholar 

  67. Farid, H.: A survey of image forgery detection techniques. IEEE Signal Process. Mag. 26, 16–25 (2009)

    Article  Google Scholar 

  68. Qazi, T., Hayat, K., Khan, S.U., Madani, S.A., Khan, I.A., Kołodziej, J., Li, H., Lin, W., Yow, K.C., Xu, C.-Z.: Survey on blind image forgery detection. Image Process IET 7, 660–670 (2013)

    Article  Google Scholar 

  69. Ansari, M.D., Ghrera, S.P., Tyagi, V.: Pixel-based image forgery detection: a review. IETE J. Educ. 55, 40–46 (2014)

    Article  Google Scholar 

  70. Lanh, T.V.L.T., Van-Chong, K.-S., Chong, K.-S., Emmanuel, S., Kankanhalli, M.S.: A survey on digital camera image forensic methods. In: 2007 IEEE International Conference on Multimedia and Expo, pp. 16–19 (2007)

  71. Mahdian, B., Saic, S.: A bibliography on blind methods for identifying image forgery. Signal Process. Image Commun. 25, 389–399 (2010)

    Article  Google Scholar 

  72. Warif, N.B.A., Wahab, A.W.A., Idris, M.Y.I.: Copy-move forgery detection: survey, challenges and future directions. J. Netw. Comput. Appl. 75, 259–278 (2016)

    Article  Google Scholar 

  73. Christlein, V., Riess, C.C., Jordan, J., Riess, C.C., Angelopoulou, E.: An evaluation of popular copy-move forgery detection approaches. IEEE Trans. Inf. Forensics Secur. 7, 1841–1854 (2012)

    Article  Google Scholar 

  74. Friedman, G.L.: Trustworthy digital camera: restoring credibility to the photographic image. IEEE Trans. Consum. Electron. 39(4), 905–910 (1993)

    Article  Google Scholar 

  75. Blythe, P., Fridrich, J.: Secure digital camera. In: Proceedings of the Digital Forensic Research Workshop (DFRWS ’04), pp. 17–19 (2004)

  76. Rivest, R.L., Shamir, A., Adleman, L.: A method for obtaining digital signatures and public-key cryptosystems. Commun. ACM 21(2), 120–126 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  77. Menezes, A.J., VanstoneOorschot, S.S.A.P.C.V.: Handbook of Applied Cryptography, 1st edn. CRC Press, Boca Raton (1996)

    Google Scholar 

  78. Cox, I.J., Miller, M.L., Bloom, J.A.: Digital Watermarking. Morgan Kaufmann Publishers, Berlin (2002). ISBN 978-1-55860-714-9

    Google Scholar 

  79. Barni, M., Bartolini, F.: Watermarking systems engineering: enabling digital assets security and other applications. In: Signal Processing and Communications. Marcel Dekker (2004)

  80. Cox, I.J., Miller, M.L., Bloom, J., Fridrich, J., Kalker, T.: Digital Watermarking and Steganography, 2nd edn. Morgan Kaufmann, San Francisco (2008)

    Google Scholar 

  81. Carneiro-Tavares, J.R., Madeiro-Bernardino-Junior, F.: Word-hunt: a LSB steganography method with low expected number of modifications per pixel. IEEE Lat. Am. Trans. 14(2), 1058–1064 (2016)

    Article  Google Scholar 

  82. Laskar, S.A., Hemachandran, K.: Steganography based on random pixel selection for efficient data hiding. Int. J. Comput. Eng. Technol. (IJCET) 4, 31–44 (2013)

    Google Scholar 

  83. Bhattacharyya, S.: Study and analysis of quality of service in different image-based steganography using Pixel Mapping Method (PMM). Int. J. Appl. Inf. Syst. (IJAIS) 2(7), 42–57 (2012)

    Google Scholar 

  84. Qazanfari, K., Safabakhsh, R.: A new steganography method which preserves histogram: generalization of LSB++. Inf. Sci. (NY) 277, 90–101 (2014)

    Article  MathSciNet  Google Scholar 

  85. Shobana, M., Manikandan, R.: Efficient method for hiding data by pixel intensity. Int. J. Eng. Technol. (IJET) 5(1), 74–80 (2013)

    Google Scholar 

  86. Ni, J., Hu, X., Shi, Y.Q.: Efficient JPEG steganography using domain transformation of embedding entropy. IEEE Signal Process. Lett. 25(6), 773–777 (2018)

    Article  Google Scholar 

  87. Ghoshal, N., Mandal, J.K.: A steganographic scheme for color image authentication (SSCIA), In: Proceedings of the international conference on recent trends in information technology, ICRTIT 2011, pp. 826–31 (2011)

  88. Ibaida, A., Khalil, I.: Wavelet-Based ECG steganography for protecting patient confidential information in point-of-Care systems. IEEE Trans. Biomed. Eng. 60(12), 3322–3330 (2013)

    Article  Google Scholar 

  89. Al-dmour, H., Al-ani, A.: A steganography embedding method based on edge identification and XOR coding. Expert Syst. Appl. 46, 293–306 (2016)

    Article  Google Scholar 

  90. Jero, S.E., Ramu, P.: Curvelets-based ECG steganography for data security. Electron. Lett. 52(4), 283–285 (2016)

    Article  Google Scholar 

  91. Tayan, O., Kabir, M.N., Alginahi, Y.M.: A hybrid digital-signature and zero-watermarking approach for authentication and protection of sensitive electronic documents. Sci. World J. 8, 1–15 (2014)

    Article  Google Scholar 

  92. Subramanya, S.R., Yi, B.K.: Digital Signatures. IEEE Potentials 25(2), 5–8 (2006)

    Article  Google Scholar 

  93. Lee, W.B., Chen, T.H.: A public verifiable copy protection technique for still images. J. Syst. Softw. 62(3), 195–204 (2002)

    Article  Google Scholar 

  94. Damara-Ardy, R., Indriani, O.R., Sari, C.A., Setiadi, D.R.I.M., Rachmawanto, E.H.: Digital image signature using triple protection cryptosystem (RSA, Vigenere, and MD5). In: 2017 International Conference on Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS), pp. 87–92 (2017)

  95. Meena, K.B., Tyagi, V.: A Deep Learning based Method for Image Splicing Detection. J. Phys. Conf. Ser. 1714, 012038 (2021)

    Article  Google Scholar 

  96. Pramanik, S., Bandyopadhyay, S.K., Ghosh, R.: Signature image hiding in color image using steganography and cryptography based on digital signature, concepts. In: 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp. 665–669 (2020)

  97. Moin, S.S., Islam, S.: Benford’s law for detecting contrast enhancement. In: 2017 Fourth International Conference on Image Information Processing (ICIIP), pp. 1–4 (2017)

  98. Friedman, G.L.: The trustworthy digital camera: restoring credibility to the photographic image. IEEE Trans. Consum. Electron. 39(4), 905–910 (1993)

    Article  Google Scholar 

  99. Rey, C., Dugelay, J.-L.: A survey of watermarking algorithms for image authentication. EURASIP J. Adv. Signal Proc. 6, 613–621 (2002)

    MATH  Google Scholar 

  100. Langelaar, G.C., Setyawan, I., Lagendijk, R.L.: Watermarking digital image and video data. A state-of-the-art overview. IEEE Signal Proc. Mag. 17(5), 20–46 (2000)

    Article  Google Scholar 

  101. Tafti, A.P., Malakooti, M.V., Ashourian, M., Janosepah, S.: Digital image forgery detection through data embedding in spatial domain and cellular automata. In: 7th International Conference on Digital Content, Multimedia Technology and its Applications (IDCTA), pp. 11–15 (2011)

  102. Bamatraf, A., Ibrahim, R., Najib, M., Salleh, M.: A new digital watermarking algorithm using combination of least significant bit (LSB) and inverse bit. J. Comput. 3(4), 2151–9617 (2011)

    Google Scholar 

  103. Sharma, P.K., Rajni: Analysis of image watermarking using least significant bit algorithm. Int. J. Inf. Sci. Tech. (IJIST) 2(4), 666–673 (2012)

    MathSciNet  Google Scholar 

  104. Bhattacharya, S.: Additive watermarking in optimized digital image. In: IEEE Beacon, IEEE (Delhi Section), 79(1) (2012)

  105. Bose, A., Maity, S.P.: Spread spectrum watermark detection on degraded compressed sensing. IEEE Sens. Lett. 1(5), 1–4 (2017)

    Article  Google Scholar 

  106. Urvoy, M., Goudia, D., Autrusseau, F.: Perceptual DFT watermarking with improved detection and robustness to geometrical distortions. IEEE Trans. Inf. Forensics Secur. 9(7), 1108–1119 (2014)

    Article  Google Scholar 

  107. Chaturvedi, N., Basha, S.J.: Comparison of Digital Image watermarking Methods DWT & DWT- DCT on the Basis of PSNR, International Journal of Innovative Research in Science, Engineering and Technology IJIRSET www.ijirset.com, 1 (2):147 (2012)

  108. Ernawan, F., Kabir, M.N.: A robust image watermarking technique with an optimal DCT-psychovisual threshold. IEEE Access 6, 20464–20480 (2018)

    Article  Google Scholar 

  109. Makbol, N.M., Khoo, B.E., Rassem, T.H.: Block-based discrete wavelet transform-singular value decomposition image watermarking scheme using human visual system characteristics. IET Image Proc. 10(1), 34–52 (2016)

    Article  Google Scholar 

  110. Mansouri, A., Aznaveh, A.M., Azar, F.T.: SVD-based digital image watermarking using complex wavelet transform. Sadhana 34(3), 393–406 (2009)

    Article  Google Scholar 

  111. Bapat, K.S., Totla, R.V.: Comparative analysis of watermarking in digital images using DCT & DWT. Int. J. Sci. Res. Publ. (IJSRP) 3(2), 1 (2013)

    Google Scholar 

  112. Mathai, N.J., Kundur, D., Sheikholeslami, A.: Hardware implementation perspectives of digital video watermarking algorithms. IEEE Trans. Signal Process. 51(4), 925–938 (2003)

    Article  Google Scholar 

  113. Kougianos, E., Mohanty, S.P., Mahapatra, R.N.: Hardware assisted watermarking for multimedia. Comput. Electr. Eng. 35(2), 339–358 (2009)

    Article  MATH  Google Scholar 

  114. Zhang, X., Wang, S.: Fragile watermarking with error free restoration capability. IEEE Trans. Multimed. 10(8), 1490–1499 (2008)

    Article  Google Scholar 

  115. Chang, J., Chen, B., Tsai, C.: LBP-based fragile watermarking scheme for image tamper detection and recovery. In: International Symposium on Next Generation Electronics (Kaohsiung, 2013), pp. 173–176 (2013)

  116. Doyoddorj, M., Rhee, K.H.: Multidisciplinary research and practice for information systems. In: IFIP WG 8.4, 8.9/TC 5 International Cross-Domain Conference and Workshop on Availability, Reliability, and Security, CD-ARES 2012, Prague, Czech Republic, August 20–24, 2012. Proceedings (ed. by (2012)

  117. Quirchmayer, G., Basl, J., You, I., Xu, L., Weippl, E.: Multidisciplinary Research and Practice for Informations Systems. Springer Publishing (2012)

    Book  Google Scholar 

  118. Tong, X., Liu, Y., Zhang, M., Chen, Y.: A novel chaos-based fragile watermarking for image tampering detection and self-recovery. Signal Process. Image Commun. 28(2), 301–308 (2013)

    Article  Google Scholar 

  119. Huo, Y., He, H., Chen, F.: Alterable-capacity fragile watermarking scheme with restoration capability. Opt. Commun. 285(7), 1759–1766 (2012)

    Article  Google Scholar 

  120. Wang, W., Men, A., Yang, B.: A feature-based semi-fragile watermarking scheme in DWT domain. In: 2010 2nd IEEE International Conference on Network Infrastructure and Digital Content, pp. 768–772 (2010)

  121. Yu, M., Wang, J., Jiang, G., Peng, Z., Shao, F., Luo, T.: New fragile watermarking method for stereo image authentication with localization and recovery. AEU Int. J. Electron. Commun. 69(1), 361–370 (2015)

    Article  Google Scholar 

  122. Lin, S.D., Kuo, Y.C., Huang, Y.H. An image watermarking scheme with tamper detection and recovery. In: First International Conference on Innovative Computing, Information and Control-Volume I (ICICIC’06), vol. 3, pp. 74–77 (2006)

  123. Zhang, H., Wang, C., Zhou, X.: Fragile watermarking for image authentication using the characteristic of SVD. Algorithms 10(1), 27 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  124. Li, C., Wang, Y., Ma, B., Zhang, Z.: A novel self-recovery fragile watermarking scheme based on dual-redundant-ring structure. Comput. Electr. Eng. 37(6), 927–940 (2011)

    Article  Google Scholar 

  125. Bravo-Solorio, S., Nandi, A.K.: Secure fragile watermarking method for image authentication with improved tampering localization and self-recovery capabilities. Sign. Proces 91(4), 728–739 (2011)

    Article  MATH  Google Scholar 

  126. Eswaraiah, R., Reddy, E.S.: ROI-based fragile medical image watermarking technique for tamper detection and recovery using variance. In: Seventh International Conference on Contemporary Computing (IC3), pp. 553–558 (2014)

  127. He, H., Chen, F., Tai, H., Member, S., Kalker, T., Zhang, J.: Performance analysis of a block-neighborhood-based self-recovery fragile watermarking scheme. IEEE Trans. Inf. Forensics Secur 7(1), 185–196 (2012)

    Article  Google Scholar 

  128. Qin, C., Ji, P., Zhang, X., Dong, J., Wang, J.: Fragile image watermarking with pixel-wise recovery based on overlapping embedding strategy. Signal Process. 138, 280–293 (2017)

    Article  Google Scholar 

  129. Zhu, X.S., Sun, Y., Meng, Q.H., Sun, B., Wang, P., Yang, T.: Optimal watermark embedding combining spread spectrum and quantization. EURASIP J. Adv. Signal Process. 1, 74 (2016)

    Article  Google Scholar 

  130. Molina-Garcia, J., Garcia-Salgado, B., Ponomaryov, V., Reyes-Reyes, R., Sadovnychiy, S., Cruz-Ramos, C.: An effective fragile watermarking scheme for color image tampering detection and self-recovery. Signal Process. Image Commun. 81, 115725 (2020)

    Article  Google Scholar 

  131. Cao, X., Fu, Z., Sun, X.: (2016). A privacy-preserving outsourcing data storage scheme with fragile digital watermarking-based data auditing. J. Electr. Comput. Eng., pp. 1–7 (2016)

  132. Abbas, N.H., Ahmad, S.M.S., Ramli, A.R.B., Parveen, S.: A multi-purpose watermarking scheme based on hybrid of lifting wavelet transform and Arnold transform. In: International Conference on Multidisciplinary in IT and Communication Science and Applications (2016)

  133. Chen, F., He, H., Tai, H.M., Wang, H.: Chaos-based self-embedding fragile watermarking with flexible watermark payload. Multimed. Tool Appl. 72(1), 41–56 (2014)

    Article  Google Scholar 

  134. Chamlawi, R., Usman, I., Khan, A.: Dual watermarking method for secure image authentication and recovery. In: IEEE 13th International Multitopic Conference (Islamabad, 2009), pp. 1–4 (2009)

  135. Yu, X., Wang, C., Zhou, X.: Review on semi-fragile watermarking algorithms for content authentication of digital images. Future Internet 9(4), 56 (2017)

    Article  Google Scholar 

  136. Wang, W., Men, A., Yang, B.: A feature-based semi-fragile watermarking scheme in DWT domain. In: 2nd IEEE International Conference on Network Infrastructure and Digital Content (Beijing, 2010), 768–772 (2010)

  137. Qi, X., Xin, X.: A singular-value-based semi-fragile watermarking scheme for image content authentication with tamper localization. J. Vis. Commun. Image Represent 30, 312–327 (2015)

    Article  Google Scholar 

  138. Huo Y., He H, Chen F (2013). Semi-fragile watermarking scheme with discriminating general tampering from collage attack, Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (Kaohsiung, 2013), 1–6.

  139. Li Y, Du L (2014). Semi-fragile watermarking for image tamper localization and self-recovery, Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) (Wuhan, 2014), 328–333.

  140. Ekici, O., Sankur, B., Coşkun, B., Naci, U., Akcay, M.: Comparative evaluation of semifragile watermarking algorithms. J. Electron. Imaging 13(1), 209 (2004)

    Article  Google Scholar 

  141. Jamal, S.S., Khan, M.U., Shah, T.: Awatermarking technique with chaotic fractional S-box transformation. Wirel. Pers. Commun. 90(4), 2033–2049 (2016)

    Article  Google Scholar 

  142. Wang, S., Zheng, D., Zhao, J., Tam, W.J., Speranza, F.: Adaptive watermarking and tree structure-based image quality estimation. IEEE Trans. Multimed. 6(2), 331–324 (2014)

    Google Scholar 

  143. Shieh, J.M., Lou, D.C., Chang, M.C.: A semi-blind digital watermarking scheme based on singular value decomposition. Comput. Stand. Interfaces 28(4), 428–440 (2006)

    Article  Google Scholar 

  144. Hsia, S.C., Jou, I.C., Hwang, S.M.: A gray level watermarking algorithm using double layer hidden. ICE Trans. Fund. Electron. ommun. Comput. Sci. 85(2), 436–471 (2002)

    Google Scholar 

  145. Rao, R.S.P, Kumar P.R.: Digital Signature based Image Watermarking using Ga and Pso. Int. J. Eng. Res. Technol. (IJERT), 6 (6), (2017)

  146. Singh, H.V., Singh, A.K., Yadav, S., Mohan, A.: DCT based secure data hiding for intellectual property right protection. CSI Trans. ICT 2(3), 163–168 (2014)

    Article  Google Scholar 

  147. Chen, T., Lu, H.: Robust spatial LSB watermarking of color images against JPEG compression. In: IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI) (Nanjing, 2012), 872–875 (2012)

  148. Wang, N., Kim, C.: Tamper detection and self-recovery algorithm of color image based on robust embedding of dual visual watermarks using DWT-SVD. In: 9th International Symposium on Communications and Information Technology (Icheon, 2009), 157–162 (2009)

  149. Abdulazeez, A.M., Zeebaree, D.Q., Hajy, D.M., Zebari, D.A.: Robust watermarking scheme based LWT and SVD using artificial bee colony optimization. Indones. J. Electric. Eng. Comput. Sci. 21(2), 1218 (2021)

    Google Scholar 

  150. Makbol, N.M., Khoo, B.E., Rassem, T.H.: Security analyses of false positive problem for the SVD-based hybrid digital image watermarking techniques in the wavelet transform domain. Multimed. Tools Appl. 77, 1–35 (2018)

    Article  Google Scholar 

  151. Ambadekar, S.P., Jain, J., Khanapuri, J.: Digital image watermarking through encryption and DWT for copyright protection. In: Recent trends in signal and image processing. Singapore: Springer, 187–195 (2019)

  152. Pan-Pan, N., Xiang-Yang, W., Yu-Nan, L., Hong-Ying, Y.: A robust color image watermarking using local invariant significant bitplane histogram. Multimed. Tools Appl. 76(3), 3403–3433 (2017)

    Article  Google Scholar 

  153. Vaidya, S.P., Mouli, P.C.: Adaptive digital watermarking for copyright protection of digital images in wavelet domain. Proc. Comput. Sci. 58, 233–240 (2015)

    Article  Google Scholar 

  154. Wu, H.T., Huang, J.: Reversible image watermarking on prediction errors by efficient histogram modification. Signal Process. 92(12), 3000–3009 (2012)

    Article  Google Scholar 

  155. Peng, F., Li, X., Yang, B.: Adaptive reversible data hiding scheme based on integer transform. Signal Process. 92(1), 54–62 (2012)

    Article  Google Scholar 

  156. Ahmed, B., Gulliver, T.A., alZahir, S.: Image splicing detection using mask-RCNN. SIViP 14, 1035–1042 (2020)

    Article  Google Scholar 

  157. Park, T.H., Han, J.G., Moon, Y.H., Eom, I.K.: Image splicing detection based on inter-scale 2D joint characteristic function moments in wavelet domain. EURASIP J. Image Video Process 30, 1–10 (2016)

    Google Scholar 

  158. Rao, Y., Ni, J.: A deep learning approach to detection of splicing and copy-move forgeries in images. In: 8th IEEE International Workshop Information Forensics Security WIFS (2016)

  159. Shen, X., Shi, Z., Chen, H.: Splicing image forgery detection using textural features based on the grey level co-occurrence matrices. IET Image Process. 11, 44–53 (2017)

    Article  Google Scholar 

  160. Bahrami, K., Member, S., Kot, A.C., Li, L., Li, H., Member, S.: Blurred image splicing localization by exposing blur type inconsistency. IEEE Trans. Inf. Forensics Secur 6013, 1–10 (2015)

    Google Scholar 

  161. Kanwal, N., Girdhar, A., Kaur, L., Bhullar, J.S.: Digital image splicing detection technique using optimal threshold based local ternary pattern. Multimed. Tools Appl. 79, 12829–12846 (2020)

    Article  Google Scholar 

  162. Jaiswal, A.K., Srivastava, R.: A technique for image splicing detection using hybrid feature set. Multimed. Tools Appl. 79(17), 11837–11860 (2020)

    Article  Google Scholar 

  163. El-Latif, E.I., Taha, A., Zayed, H.: A passive approach for detecting image splicing using deep learning and Haar Wavelet Transform. Int. J. Comput. Netw. Inf. Secur. 11, 28–35 (2019)

    Google Scholar 

  164. Jaiprakash, S.P., Desai, M.B., Prakash, C.S., Mistry, V.H., Radadiya, K.L.: Low dimensional DCT and DWT feature based model for detection of image splicing and copy-move forgery. Multimed. Tools Appl. 79, 29977–30005 (2020)

    Article  Google Scholar 

  165. Niyishaka, P., Bhagvati, C.: Image splicing detection technique based on Illumination-Reflectance model and LBP. Multimed. Tools Appl. 80, 2161–2175 (2021)

    Article  Google Scholar 

  166. Prabhu-Kavin, B., Ganapathy, S., Suthanthiramani, P., Kannan, A. A modified digital signature algorithm to improve the biomedical image integrity in cloud environment. In: Advances in Computational Techniques for Biomedical Image Analysis, 253–271 (2020)

  167. Sharma, V., Jha, S., Bharti, R.K.: Image forgery and it’s detection technique: a review. Int. Res. J. Eng. Technol. (IRJET) 3(3), 756–762 (2016)

    Google Scholar 

  168. Yang, B., Sun, X., Guo, H., Xia, Z., Chen, X.: A copy-move forgery detection method based on CMFD-SIFT. Multimed. Tools Appl. 77, 837–855 (2017)

    Article  Google Scholar 

  169. Meena, K.B., Tyagi, V.: A copy-move image forgery detection technique based on tetrolet transform. J. Inf. Secur. Appl. 52, 2481 (2020)

    Google Scholar 

  170. Zhu, Y., Shen, X., Chen, H.: Copy-move forgery detection based on scaled ORB. Multimed. Tools Appl. 75, 3221–3233 (2016)

    Article  Google Scholar 

  171. Huang, D., Huang, C., Hu, W.: Robustness of copy-move forgery detection under high JPEG compression artifacts. Multimed. Tools Appl. 76(1), 1509–1530 (2017)

    Article  Google Scholar 

  172. Islam, A., Long, C., Basharat, A., Hoogs, A.: DOA-GAN: Dual-order attentive generative adversarial network for image copy-move forgery detection and localization. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4675–4684 (2020)

  173. Zhu, Y., Chen, C., Yan, G., Guo, Y., Dong, Y.: AR-Net: adaptive attention and residual refinement network for copy-move forgery detection. IEEE Trans. Ind. Inf. 16, 1–1 (2020)

    Article  Google Scholar 

  174. Zandi, M., Mahmoudi-Aznaveh, A., Talebpour, A.: Iterative copy-move forgery detection based on a new interest point detector. IEEE Trans. Inf. Forensics Secur. 11, 2499–2512 (2016)

    Article  Google Scholar 

  175. Bi, X., Pun, C.M., Yuan, X.C.: Multi-level dense descriptor and hierarchical feature matching for copy-move forgery detection. Inf. Sci. (Ny) 345, 226–242 (2016)

    Article  Google Scholar 

  176. Silva, E., Carvalho, T., Ferreira, A., Rocha, A.: Going deeper into copy-move forgery detection: exploring image telltales via multi-scale analysis and voting processes. J. Vis. Commun. Image Represent. 29, 16–32 (2015)

    Article  Google Scholar 

  177. Bi, X.L., Pun, C.M., Yuan, X.C.: Multi-scale feature extraction and adaptive matching for copy-move forgery detection. Multimed. Tools. Appl. 77, 1–23 (2016)

    Google Scholar 

  178. Wenchang, S.H.I., Fei, Z., Bo, Q.I.N., Bin, L.: Improving image copy-move forgery detection with particle swarm optimization techniques. China Commun. 13, 139–149 (2016)

    Article  Google Scholar 

  179. Pun, C., Member, S., Yuan, X., Bi, X.: Oversegmentation and feature point matching. IEEE Trans. Inf. Forensics Secur. 10, 1705–1716 (2015)

    Article  Google Scholar 

  180. Wang, X.Y., Li, S., Liu, Y.N., Niu, Y., Yang, H.Y., Zhou, Z.: A new keypoint-based copy-move forgery detection for small smooth regions. Multimed. Tools Appl. 76(22), 23353–23382 (2016)

    Article  Google Scholar 

  181. Li, J., Li, X., Yang, B., Sun, X.: Segmentation-based image copy-move forgery detection scheme. IEEE Trans. Inf. Forensics Secur. 10(3), 507–518 (2015)

    Article  Google Scholar 

  182. Zheng, J., Liu, Y., Ren, J., Zhu, T., Yan, Y., Yang, H.: Fusion of block and keypoints based approaches for effective copy-move image forgery detection. Multidimens. Syst. Signal Process. 27, 989–1005 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  183. Park, J.Y., Kang, T.A., Moon, Y.H., Eom, I.K.: Copy-move forgery detection using scale invariant feature and reduced local binary pattern histogram. Symmetry 12, 492 (2020)

    Article  Google Scholar 

  184. Niyishaka, P., Bhagvati, C.: Copy-move forgery detection using image blobs and BRISK feature. Multimed. Tools Appl. 79, 26045–26059 (2020)

    Article  Google Scholar 

  185. Tinnathi, S.G.: An efficient copy move forgery detection using adaptive watershed segmentation with AGSO and hybrid feature extraction. J. Vis. Commun. Image Represent. 74, 1966 (2021)

    Article  Google Scholar 

  186. Nguyen, H.C., Katzenbeisser, S.: Robust resampling detection in digital images. In: International Conference on Communications and Multimedia Security, pp. 3–15 (2012)

  187. Flenner, A., Peterson, L., Bunk, J., Mohammed, T.M., Nataraj, L., Manjunath, B.S.: Resampling forgery detection using deep learning and A-contrario analysis. Electron. Imaging 7, 2121–2127 (2018)

    Article  Google Scholar 

  188. Vazquez-Padin, D., Perez-Gonzalez, F., Comesana-Alfaro, P.: A random matrix approach to the forensic analysis of upscaled images. IEEE Trans. Inf. Forensics Secur. 12(9), 2115–2130 (2017)

    Article  Google Scholar 

  189. Qiao, T., Zhu, A., Retraint, F.: Exposing image resampling forgery by using linear parametric model. Multimed. Tools Appl. 77, 1501–1523 (2018)

    Article  Google Scholar 

  190. Bayar B, Stamm M.C.: On the robustness of constrained convolutional neural networks to JPEG post-compression for image resampling detection. In: IEEE International Conference on Acoustics Speech Signal Process, pp. 2152–2156 (2017)

  191. Lamba, M., Mitra, K.: Multi-patch aggregation models for resampling detection. In: ICASSP 2020—2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2967–2971 (2010)

  192. Peng, A., Wu, Y., Kang, X.: Revealing traces of image resampling and resampling antiforensics. Adv. Multimed., pp. 1–13 (2017)

  193. Bharathiraja, S., Rajesh Kanna, B.: Anti-forensics contrast enhancement detection (AFCED) technique in images based on laplace derivative histogram. Mob. Netw. Appl. 24, 1174–1180 (2019)

    Article  Google Scholar 

  194. Lin, X., Li, C., Hu, Y.: Exposing image forgery through the detection of contrast enhancement. In: International Conference on Image Process, pp. 4467–4471 (2013)

  195. Zhu, N., Deng, C., Gao, X.: Image sharpening detection based on multiresolution overshoot artifact analysis. Multimed. Tools Appl. 76, 16563–16580 (2017)

    Article  Google Scholar 

  196. Stamm, M., Ray, K.J.: Blind forensics of contrast enhancement in digital images. In: Proceedings of International Conference on Image Process ICIP, 3112–3115 (2008)

  197. Cao, G., Zhao, Y., Ni, R., Kot, A.C.: Unsharp masking sharpening detection via overshoot artifacts analysis. IEEE Signal Process. Lett. 18(10), 603–606 (2011)

    Article  Google Scholar 

  198. Cao G, Zhao Y, Ni R (2009). Detection of image sharpening based on histogram aberration and ringing artifacts, 2009 IEEE International Conference on Multimedia and Expo.,1026–1029

  199. Ding, F., Zhu, G., Yang, J., Xie, J., Shi, Y.-Q.: Edge perpendicular binary coding for USM sharpening detection. IEEE Signal Process. Lett. 22(3), 327–331 (2015)

    Article  Google Scholar 

  200. Wang, Q., Zhang, R.: Double JPEG compression forensics based on a convolutional neural network. EURASIP J. Inf. Secur. 1, 23 (2016)

    Article  Google Scholar 

  201. Madhusudhan, K.N., Sakthivel, P.: Combining digital signature with local binary pattern-least significant bit steganography techniques for securing medical images. J. Med. Imaging Health Inf. 10(6), 1288-1293 (6) (2020)

    Article  Google Scholar 

  202. Shan, W., Yi, Y., Huang, R., Xie, Y.: Robust contrast enhancement forensics based on convolutional neural networks. Signal Process. Image Commun. 71, 138–146 (2018)

    Article  Google Scholar 

  203. Zhang, C., Du, D., Ke, L., Qi, H., Lyu, S.: Global contrast enhancement detection via deep multi-path network. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2815–2820 (2018)

  204. Barni, M., Bondi, L., Bonettini, N., Bestagini, P., Costanzo, A., Maggini, M., Tondi, B., Tubaro, S.: Aligned and non-aligned double JPEG detection using convolutional neural networks. J. Vis. Commun. Image Represent. 49, 153–163 (2017)

    Article  Google Scholar 

  205. Zhang, Y., Thing, V.L.L.: A semi-feature learning approach for tampered region localization across multi-format images. Multimed. Tools Appl. 77, 25027–25052 (2018)

    Article  Google Scholar 

  206. Zeng, X., Feng, G., Zhang, X.: Detection of double JPEG compression using modified DenseNet model. Multimed. Tools Appl. 78, 8183–8196 (2018)

    Article  Google Scholar 

  207. Peng, P., Sun, T., Jiang, X., Xu, K., Li, B., Shi, Y.: Detection of double JPEG compression with the same quantization matrix based on convolutional neural networks. In: 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC., pp. 717–721 (2018)

  208. Ahn, W., Nam, S.-H., Son, M., Lee, H.K., Choi, S.: End-to-end double JPEG detection with a 3D convolutional network in the DCT domain. Electron. Lett. 56, 82–85 (2020)

    Article  Google Scholar 

  209. Amerini, I., Uricchio, T., Ballan, L., Caldelli, R. Localization of JPEG double compression through multi-domain convolutional neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 1865-1871 (2017)

  210. Verma, V., Singh, D., Khanna, N.: Block-level double JPEG compression detection for image forgery localization. In: arXiv: Image and Video Processing (2020)

  211. Lukas, J., Fridrich, J., Goljan, M.: Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 2(1), 205–214 (2006)

    Article  Google Scholar 

  212. Bayram, S., Sencar, H.T., Memon, N., Avcibas, I.: Source camera identification based on CFA interpolation. In: IEEE International Conference on Image Processing (ICIP) 2005 (2005)

  213. Kharrazi, M., Sencar, H.T., Memon, N.: Blind source camera identification. In: IEEE International Conference on Image Processing (ICIP) 2004 (2004)

  214. Taspinar, S., Mohanty, M., Memon, N.: PRNU-based camera attribution from multiple seam-carved images. IEEE Trans. Inf. Forensics Secur. 12(12), 3065–3080 (2017)

    Article  Google Scholar 

  215. Xu, B., Wang, X., Zhou, X., Xi, J., Wang, S.: Source camera identification from image texture features. Neurocomputing 207, 131–140 (2016)

    Article  Google Scholar 

  216. Hsu, Y.-F., Chang, S.-F.: Camera response functions for image forensics: an automatic algorithm for splicing detection. IEEE Trans. Inf. Forensics Secur. 5(4), 816–825 (2010)

    Article  MathSciNet  Google Scholar 

  217. Zheng L, Sun T, Shi Y. Q (2014). Inter-frame video forgery detection based on block-wise brightness variance descriptor. In: International workshop on digital watermarking, Springer, 18–30.

  218. Liu, H., Li, S., Bian, S.: Detecting frame deletion in h. 264 video. In: International Conference on Information Security Practice and Experience, Springer, pp. 262–270 (2014)

  219. Yao, H., Ni, R., Zhao, Y.: An approach to detect video frame deletion under anti-forensics. J. Real-Time Image Proc. 16(3), 751–764 (2019)

    Article  Google Scholar 

  220. Shanableh, T.: Detection of frame deletion for digital video forensics. Digit. Invest. 10(4), 350–360 (2013)

    Article  Google Scholar 

  221. Long, C., Smith, E., Basharat, A., Hoogs, A.: A c3d-based convolutional neural network for frame dropping detection in a single video shot. In: 2017 IEEE Conference on computer vision and pattern recognition workshops (CVPRW) IEEE, 1898–1906 (2017)

  222. Bayar, B., Stamm, M. C.: A deep learning approach to universal image manipulation detection using a new convolutional layer. In: Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, pp. 5–10 (2016)

  223. Cozzolino, D., Verdoliva, L.: Single-image splicing localization through autoencoder-based anomaly detection. In: 2016 IEEE International Workshop on Information Forensics and Security (WIFS), 1–6 (2016)

  224. Fadl, S.M., Han, Q., Li, Q.: Authentication of surveillance videos: detecting frame duplication based on residual frame. J Forensic Sci. 63(4), 1099–1109 (2018)

    Article  Google Scholar 

  225. Pandey, R.C., Singh, S.K., Shukla, K.: Passive copy-move forgery detection in videos. In: 2014 International Conference on Computer and Communication Technology (ICCCT). IEEE, pp. 301–306 (2014)

  226. Hu, Y., Li, C.T., Wang, Y., Liu, B.B.: An improved fingerprinting algorithm for detection of video frame duplication forgery. Int. J. Digit. Crime Forensics (IJDCF) 4(3), 20–32 (2012)

    Article  Google Scholar 

  227. Lin, G.S., Chang, J.F.: Detection of frame duplication forgery in videos based on spatial and temporal analysis. Int. J. Pattern Recognit. Artif. Intell. 26(07), 1250017 (2012)

    Article  MathSciNet  Google Scholar 

  228. Liao, S.Y., Huang, T.Q.: Video copy-move forgery detection and localization based on Tamura texture features. In: 2013 6th International Congress on Image and Signal Processing (CISP), vol. 2, pp. 864–868 (2013)

  229. Li, F., Huang, T.: Video copy-move forgery detection and localization based on structural similarity. In: Proceedings of the 3rd International Conference on Multimedia Technology (ICMT 2013 Springer, 63–76 (2014)

  230. Chao, J., Jiang, X., Sun, T.: A novel video inter-frame forgery model detection scheme based on optical flow consistency. In: International Workshop on Digital Watermarking. Springer, 267–281 (2012)

  231. Kang, X., Liu, J., Liu, H., Wang, Z.J.: Forensics and counter anti-forensics of video inter-frame forgery. Multimed. Tools Appl. 75(21), 13833–13853 (2016)

    Article  Google Scholar 

  232. Stamm, M.C., Lin, W.S., Liu, K.J.R.: Temporal forensics and anti-forensics for motion compensated video. IEEE Trans. Inf. Forensics Sec. 7(4), 1315–1329 (2012)

    Article  Google Scholar 

  233. Wang, Q., Li, Z., Zhang, Z., Ma, Q.: Video inter-frame forgery identification based on consistency of correlation coefficients of gray values. J Comput. Commun. 2(04), 51 (2014)

    Article  Google Scholar 

  234. Aghamaleki, J.A., Behrad, A.: Malicious inter-frame video tampering detection in mpeg videos using time and spatial domain analysis of quantization effects. Multimed. Tools Appl 76(20), 20691–20717 (2017)

    Article  Google Scholar 

  235. Aghamaleki, J.A., Behrad, A.: Inter-frame video forgery detection and localization using intrinsic effects of double compression on quantization errors of video coding. Signal Process. Image Commun. 47, 289–302 (2016)

    Article  Google Scholar 

  236. Wang, W., Farid, H.: Exposing digital forgeries in video by detecting double quantization, In: Proceedings of the 11th ACM Workshop on Multimedia and Security, 39–48 (2009)

  237. Wang W, Jiang X, Wang S, Wan M, Sun T (2013). Identifying video forgery process using optical flow, In: International workshop on digital watermarking. Springer, 244–257.

  238. Ravi, H., Subramanyam, A.V., Gupta, G., Kumar, B.A.: Compression noise based video forgery detection. In: 2014 IEEE International Conference on Image Processing (ICIP). IEEE, pp. 5352–5356 (2014)

  239. Wang, W., Farid, H.: Exposing digital forgeries in video by detecting duplication. In: Proceedings of the 9th Workshop on Multimedia & Security, pp 35–42 (2007)

  240. Singh, R.D., Aggarwal, N.: Detection and localization of copy-paste forgeries in digital videos. Forensic Sci. Int. 281, 75–91 (2017)

    Article  Google Scholar 

  241. Chetty, G., Biswas, M., Singh, R.: Digital video tamper detection based on Multimodal fusion of residue features. In: 2010 Fourth international Conference on Network and System Security. IEEE, pp. 606–613 (2010)

  242. Yao, Y., Shi, Y., Weng, S., Guan, B.: Deep learning for detection of object-based forgery in advanced video. Symmetry 10(1), 3 (2017)

    Article  Google Scholar 

  243. Saddique, M., Asghar, K., Bajwa, U.I., Hussain, M., Habib, Z.: Spatial video forgery detection and localization using texture analysis of consecutive frames. Adv. Elect Comput. Eng. 19(3), 97–108 (2019)

    Article  Google Scholar 

  244. Chen, R., Dong, Q., Ren, H., Fu, J.: Video forgery detection based on non-subsampled contourlet transform and gradient information. Inf. Technol. J. 11(10), 1456–1462 (2012)

    Article  Google Scholar 

  245. Aloraini, M., Sharifzadeh, M., Agarwal, C., Schonfeld, S.: Statistical sequential analysis for object- based video forgery detection. Elect. Image 5, 543–551 (2019)

    Google Scholar 

  246. Aloraini, M., Sharifzadeh, M., Schonfeld, D.: Sequential and patch analyses for object removal video forgery detection and localization. IEEE Trans. Circ. Syst. Vid. Technol. 31, 917–930 (2020)

    Article  Google Scholar 

  247. Kobayashi, M., Okabe, T., Sato, Y.: Detecting forgery from static-scene video based on inconsistency in noise level functions. IEEE Trans. Inf. Forensics Sec. 5(4), 883–892 (2010)

    Article  Google Scholar 

  248. Wang, W., Farid, H.: Exposing digital forgeries in interlaced and deinterlaced video. IEEE Trans. Inf. Forensics Sec. 2(3), 438–449 (2007)

    Article  Google Scholar 

  249. Labartino, D., Bianchi, T., De Rosa, A., Fontani, M., Va´zquez-Pad´ın, D., Piva, A., Barni, M.: Localization of forgeries in mpeg-2 video through GOP size and DQ analysis. In: 2013 IEEE 15th International Workshop on Multimedia Signal Processing (MMSP). IEEE, vol. 2, pp. 494–499 (2013)

  250. Subramanyam, A.V., Emmanuel, S.: Video forgery detection using hog features and compression properties. In: 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP). IEEE, pp. 89–94 (2012)

  251. Hsu, C.C., Hung, T.Y., Lin, C.W., Hsu, C.T.: Video forgery detection using correlation of noise residue. In: 2008 IEEE 10th Workshop on Multimedia Signal Processing. IEEE, 170–174 (2008)

  252. Kancherla, K., Mukkamala, S.: Novel blind video forgery detection using markov models on motion residue. In: Asian Conference on Intelligent Information and Database Systems. Springer, 308–315 (2012)

  253. Fayyaz, M.A., Anjum, A., Ziauddin, S., Khan, A., Sarfaraz, A.: An improved surveillance video forgery detection technique using sensor pattern noise and correlation of noise residues. Multimed. Tools Appl. 79(9), 5767–5788 (2020)

    Article  Google Scholar 

  254. Singh, R.D., Aggarwal, N.: Detection of upscale-crop and splicing for digital video authentication. Digit. Invest. 21, 31–52 (2017)

    Article  Google Scholar 

  255. Chen, J., Kang, X., Liu, Y., Wang, Z.J.: Median filtering forensics based on convolutional neural networks. IEEE Signal Process. Lett. 22(11), 1849–1853 (2015)

    Article  Google Scholar 

  256. Hyun, D.K., Lee, M.J., Ryu, S.J., Lee, H.Y., Lee, H.K.: Forgery detection for surveillance video. In: The era of interactive media. Springer, pp. 25–36 (2013)

  257. Zhang, Y., Goh, J., Win, L.L., Thing, V.L.: Image region forgery detection: A deep learning approach. In: SG-CRC, pp. 1–11 (2016)

  258. Rao, Y., Ni, J.: A deep learning approach to detection of splicing and copy-move forgeries in images. In: 2016 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6 (2016)

  259. Bondi, L., Lameri, S., Güera, D., Bestagini, P., Delp, E.J., Tubaro, S.: Tampering detection and localization through clustering of camera-based cnn features. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1855–1864 (2017)

  260. Amerini, I., Uricchio, T., Ballan, L., Caldelli, R.: Localization of jpeg double compression through multi-domain convolutional neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1865–1871 (2017)

  261. Salloum, R., Ren, Y., Jay, K.C.-C.: Image splicing localization using a multi-task fully convolutional network (MFCN). J. Vis. Commun. Image Represent. 51, 201–209 (2018)

    Article  Google Scholar 

  262. Wu, Y., Abd-Almageed, W., Natarajan, P.: Busternet: detecting copy-move image forgery with source/target localization. In: Proceedings of the European Conference on Computer Vision (ECCV), 168–184 (2018)

  263. Bi, X., Wei, Y., Xiao, B., Li, W.: Rru-net: The ringed residual u-net for image splicing forgery detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, (2019)

  264. Wang, X., Wang, H., Niu, S., Zhang, J.: Detection and localization of image forgeries using improved mask regional convolutional neural network. Math. Biosci. Eng. 16, 4581–4593 (2019)

    Article  Google Scholar 

  265. Kumar, K., Shrimankar, D.D., Singh, N.: Event bagging: A novel event summarization approach in multiview surveillance videos. In: 2017 International Conference on Innovations in Electronics, Signal Processing and Communication (IESC), pp. 106–111 (2017)

  266. Gunawardena, P., Sudarshana, H., Amila, O., Nawaratne, R., Alahakoon, D., Perera, A.S., Chitraranjan, C.: Interest-oriented video summarization with keyframe extraction. In: 2019 19th International Conference on Advances in ICT for Emerging Regions, 250:1–8 (2019)

  267. Xia, G., Chen, B., Sun, H., Liu, Q.: Nonconvex low-rank kernel sparse subspace learning for keyframe extraction and motion segmentation. IEEE Trans. Neural Netw. Learn. Syst. 32, 1–15 (2020)

    MathSciNet  Google Scholar 

  268. Kumar, K., Shrimankar, D.D.: Deep event learning boost-up approach: Delta. Multimed. Tools Appl. 77, 26635–26655 (2018)

    Article  Google Scholar 

  269. Kumar, K., Shrimankar, D.D.: F-des: Fast and deep event summarization. IEEE Trans. Multimed. 20(2), 323–334 (2018)

    Article  Google Scholar 

  270. Kumar, K., Shrimankar, D.D., Singh, N.: Eratosthenes sieve based key-frame extraction technique for event summarization in videos. Multimed. Tools Appl. 77, 7383–7404 (2018)

    Article  Google Scholar 

  271. Sharma, S., Kumar, K. GUESS: Genetic uses in video encryption with secret sharing. Adv. Intell. Syst. Comput., 51–62 (2018)

  272. Sharma, S., Shivhare, S.N., Singh, N., Kumar, K. Computationally efficient ANN model for small-scale problems. Mach. Intell. Signal Anal., 423–435 (2018)

  273. Kumar, K.: Text query based summarized event searching interface system using deep learning over cloud. Multimed. Tools Appl. 80(7), 11079–11094 (2021)

    Article  Google Scholar 

  274. Manupriya, P., Sinha, S., Kumar, K. V⊕SEE: Video secret sharing encryption technique. In: 2017 Conference on Information and Communication Technology (CICT) (2017)

  275. Koppanati, R.K., Kumar, K., Qamar, S.: E-MOC: an efficient secret Sharing Model for Multimedia on Cloud. In: Tripathi, M., Upadhyaya, S. (eds.) Conference Proceedings of ICDLAIR2019, p. 175 (2021)

  276. Gadicha, A.B., Gupta, V.B., Gadicha, V.B., Kumar, K., Ghonge M.M.: Multimode approach of data encryption in images through QUANTUM STEGANOGRAPHY. In: Multidisciplinary Approach to Modern Digital Steganography, pp. 99–124 (2021)

  277. Xiao, J., Zhao, R., Lam, K.-M.: Bayesian sparse hierarchical model for image de-noising. Signal Process. Image Commun. 96, 116299 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Contributions

SYED TUFAEL NABI: Writing—original draft, Data Collection, Relevant Articles Collection. Munish Kumar and Paramjeet Singh: Review Protocol, Testing, Writing—review & editing. Naveen Aggarwal and Krishan Kumar: Writing—review & editing.

Corresponding author

Correspondence to Munish Kumar.

Ethics declarations

Conflict of Interest

All the authors declare that they have no conflict of interest in this work.

Additional information

Communicated by Y. Zhang.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nabi, S.T., Kumar, M., Singh, P. et al. A comprehensive survey of image and video forgery techniques: variants, challenges, and future directions. Multimedia Systems 28, 939–992 (2022). https://doi.org/10.1007/s00530-021-00873-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-021-00873-8

Keywords

Navigation