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A detailed analysis of image and video forgery detection techniques

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Abstract

With the recent advancement in modern technology, one can easily manipulate a digital image or video using computer software or a mobile application. The purpose of editing visual media could be as simple as to look good before sharing to the social networking site’s or can be as malicious as to defame or hurt one’s reputation in the real world through such morphed visual imagery. Identity theft is one of the examples where one’s identity get stolen by some impersonator who can access the personal and financial information of an innocent person. To avoid such drastic situations, law enforcement authorities must use some automatic tools and techniques to find out whether a person is innocent or the culprit. One major question that arises here is how and what parts of visual imagery can be manipulated or edited. The answer to this question is important to distinguish the authentic images/videos from the doctored multimedia. This survey provides a detailed analysis of image and video manipulation types, popular visual imagery manipulation methods, and state-of-the-art image and video forgery detection techniques. It also surveys different fake image and video datasets used in tampering. The goal is to develop a sense of privacy and security in the research community. Finally, it focuses to motivate researchers to develop generalized methods to capture artificial visual imagery which is capable of detecting any type of manipulation in given visual imagery.

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Notes

  1. https://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/AuthSplicedDataSet.html.

  2. http://elib.cs.berkeley.edu/photos/use.html.

  3. https://www.ee.columbia.edu/ln/dvmm/downloads/authsplcuncmp/.

  4. https://github.com/namtpham/casia1groundtruth.

  5. https://ieee-dataport.org/open-access/modified-casia#files.

  6. http://lci.micc.unifi.it/labd/2015/01/copy-move-forgery-detection-and-localization/.

  7. https://www5.cs.fau.de/research/data/image-manipulation/.

  8. https://www.vcl.fer.hr/comofod/.

  9. https://signalprocessingsociety.org/newsletter/2013/06/ifs-tc-image-forensics-challenge.

  10. https://mklab.iti.gr/results/the-wild-web-tampered-image-dataset/.

  11. https://www.nist.gov/itl/iad/mig/open-media-forensics-challenge.

  12. https://mfc.nist.gov.

  13. https://github.com/wenbihan/coverage.

  14. http://loki.disi.unitn.it/RAISE/index.php.

  15. https://qualinet.github.io/databases/image/uncompressed_colour_image_database_ucid/.

  16. https://github.com/shaoanlu/faceswap-GAN.

  17. https://github.com/ondyari/FaceForensics.

  18. https://www.youtube.com/channel/UCKpH0CKltc73e4wh0_pgL3g.

  19. GitHub: https://github.com/EndlessSora/DeeperForensics-1.0.

  20. https://github.com/deepfakes/faceswap.

References

  1. Carrington, D.: (2020 Website: Mylio.com) How Many Photos in 2020? Detailed report here (2020).https://focus.mylio.com/tech-today/how-many-photos-will-be-taken-in-2020

  2. Gyncild, A.C., Team, B., Team: Adobe Photoshop CC Classroom in a Book. Pearson Education, London (2013)

    Google Scholar 

  3. The GIMP Development Team: GIMP. Retrieved from (2019)

  4. Zhang, W.: Smartphone photography in urban China. World Acad. Sci. Eng. Technol., Int. J. 960 Soc. Behav. Educ. Econ. Bus. Ind. Eng. 11(1), 231–239 (2017)

    Google Scholar 

  5. Chouhan, S.S., Kaul, A., Singh, U.P.: Image segmentation using computational intelligence techniques. Arch. Comput. Methods Eng. 26(3), 533–596 (2019)

    Article  MathSciNet  Google Scholar 

  6. Sneumueller.: Auto face swap (2016). https://www.microsoft.com/en-us/store/p/auto-face-swap/9nblggh3m5nq

  7. LTD RTP.: Face swap booth—photo faceswap and face changer (2017). https://itunes.apple.com/us/app/ face-swap-booth-photo-faceswap-face-changer/id826921329?mt=8

  8. The Economic Times Bureau.: Detailed report at: https://economictimes.indiatimes.com/tech/internet/4-in-10-indians-have-experienced-identity-theft-report/articleshow/75029916.cms?from=mdr (2020)

  9. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y.: Generative adversarial nets. Adv. Neural Inf. Process. Syst. pp. 2672–2680 (2014)

  10. Ledig, C., Theis, L., F. Husz ar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al.: Photo-realistic singleimage super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017)

  11. AI can now create fake porn, making revenge porn even more complicated. The conversation media group (2018). http://theconversation.com/ai-can-now-create-fake-pornmaking-revengeporn-even-more-complicated-92267

  12. Rose Eveleth (BBC News 13th December 2012), Detailed article here: https://www.bbc.com/future/article/20121213-fake-pictures-make-real-memories (2012)

  13. Thyagharajan, K.K., Kalaiarasi, G.: A review on near-duplicate detection of images using computer vision techniques. Arch. Comput. Methods Eng. pp 1–20 (2020)

  14. Rob Toews.: (Forbes 25May 2020). Detailed article here: https://www.forbes.com/sites/robtoews/2020/05/25/deepfakes-are-going-to-wreak-havoc-on-society-we-are-not-prepared/?sh=c2edef574940 (2020)

  15. Farid H.: Creating and detecting doctored and virtual images: implications to the child pornography prevention act. Department of Computer Science, Dartmouth College, TR2004-518, 13 (2004)

  16. Farid H.: Photo tampering throughout history. Read more at: https://www.cc.gatech.edu/~beki/cs4001/history.pdf

  17. Zhu, J., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 22–29, pp. 2242–2251 (2017)

  18. Wang, W., Dong, J., Tanm T.: A survey of passive image tampering detection. In: IWDW, 9, pp. 308–322 . Springer (2009)

  19. He, J., Lin, Z., Wang, L., Tang, X.: Detecting doctored JPEG images via DCT coefficient analysis. In: Proceedings of ECCV., pp 423–435 (2006)

  20. 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), 1841–54 (2012)

    Article  Google Scholar 

  21. Ng, T.T., Chang, S.F., Sun, Q.: Blind detection of photomontage using higher order statistics. In: Proceedings of ISCAS, vol. 5. IEEE (2004)

  22. Schetinger, V., Oliveira, M.M., da Silva, R., Carvalho, T.J.: Humans are easily fooled by digital images. Comput. Gr. (2017)

  23. Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., and Efros, A.: Context encoders: feature learning by inpainting. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2536–2544 (2016)

  24. Yang, C., Lu, X., Lin, Z., Shechtman, E., Wang, O., Li, H.: High-resolution image inpainting using multi-scale neural patch synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6721–6729 (2017)

  25. Iizuka, S., Simo-Serra, E., Ishikawa, H.: Globally and locally consistent image completion. ACM Trans. Gr. (ToG) 36(4), 1–14 (2017)

    Article  Google Scholar 

  26. Johnson, M.K., Farid, H.: Exposing digital forgeries by detecting inconsistencies in lighting. In: Proceedings of the 7th Workshop on Multimedia and Security, pp. 1–10, ACM (2005)

  27. Wen, B., Zhu, Y., et al.: COVERAGE: A novel database for copy-move forgery detection. In: Proceedings of ICIP. IEEE, 161–165 (2016)

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

  29. Bunk, J., Bappy, J.H., Mohammed, T.M., Nataraj, L., Flenner, A., Manjunath, B., Chandrasekaran, S., Roy-Chowdhury, A.K., Peterson, L.: Detection and localization of image forgeries using resampling features and deep learning. In: Proceedings of CVPRW. IEEE, pp. 1881–1889 (2017)

  30. Rao, Y., Ni, J.: A deep learning approach to detection of splicing and copy-move forgeries in images. In: Proceedings of WIFS (2016)

  31. Shi, Y.Q., Chen, C., Chen, W.: A natural image model approach to splicing detection. In: Proceedings of MM & Sec. ACM, 2007:51–62 (2007)

  32. Ng, A.: Machine learning yearning (2018). (http://www.mlyearning.org/)

  33. Tang, S.: Lessons learned from the training of GANs on artificial datasets. IEEE Access 8, 165044–165055 (2020)

    Article  Google Scholar 

  34. Schonfeld, E., Schiele, B., Khoreva, A.: A u-net based discriminator for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)

  35. Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019)

  36. Karras, T. et al.: Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)

  37. Singh, K.K., Ojha, U., Lee, Y.J.: Finegan: Unsupervised hierarchical disentanglement for fine-grained object generation and discovery. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019)

  38. Ng, T.T., Chang, S.F., Sun, Q.: A data set of authentic and spliced image blocks. Columbia University, ADVENT Technical Report (2004)

  39. Hsu, Y.F., Chang, S.F.: Detecting image splicing using geometry invariants and camera characteristics consistency. In: Proceedings of ICME (2006)

  40. Dong, J, Wang, W., and Tan, T.: CASIA image tampering detection evaluation database. In: 2013 IEEE China Summit and International Conference on Signal and Information Processing, Beijing, pp. 422–426 (2013)

  41. Zheng, Y., Cao, Y., Chang, C.: A PUF-based data-device hash for tampered image detection and source camera identification. IEEE Trans. Inf. Forensics Secur. 15, 620–634 (2020)

    Article  Google Scholar 

  42. 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), 1099–110 (2011)

    Article  Google Scholar 

  43. 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), 659–69 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  45. Bappy, J.H., Simons, C., Nataraj, L., Manjunath, B.S., Roy-Chowdhury, A.K.: Hybrid LSTM and encoder–decoder architecture for detection of image forgeries. IEEE Trans. Image Process. 28(7), 3286–3300 (2019). https://doi.org/10.1109/TIP.2019.2895466

    Article  MathSciNet  MATH  Google Scholar 

  46. IEEE IFS-TC Image Forensics Challenge Dataset. http://ifc.recod.ic.unicamp.br/fc.website/index.py

  47. Tralic, D., Zupancic, I., Grgic, S., Grgic, M.: CoMoFoD—new database for copy-move forgery detection. In: Proceedings of ELMAR (2013)

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

  49. Bas, P., Filler, T., Pevný, T.: Break our steganographic system: the ins and outs of organizing BOSS. In: International Workshop on Information Hiding. Springer, Berlin, Heidelberg (2011)

  50. Xie, D., et al.: Scut-fbp: A benchmark dataset for facial beauty perception. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics. IEEE (2015)

  51. Wen, B., Zhu, Y., Subramanian, R., Ng, T., Shen, X., Winkler, S.: COVERAGE—A novel database for copy-move forgery detection. In: Proc. IEEE Int. Conf. Image Processing (ICIP) (2016)

  52. Schaefer, G., Stich, M.: UCID—An Uncompressed Colour Image Database. In: Proc. SPIE, Storage and Retrieval Methods and Applications for Multimedia 2004, pp. 472–480, San Jose, USA (2004)

  53. Dolhansky, Brian, H., Russ, P., Ben, B., Nicole, F., Cristian C. (eds.): The deepfake detection challenge (dfdc). Preview dataset (2019). arXiv preprint, arXiv:1910.08854

  54. Xin Y., Yuezun L., and Siwei L.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp 8261–8265 (2019)

  55. Fakeapp. https://www.fakeapp.com/. Accessed: 2018-09-01

  56. Korshunov, P., Marcel, S.: Deepfakes: a new threat to face recognition? Assessment and detection (2018). arXiv preprint arXiv:1812.08685

  57. Rössler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Nießner, M.: Faceforensics: A large-scale video dataset for forgery detection in human faces (2018). arXiv preprint arXiv:1803.09179

  58. Justus T., Michael Z., Marc S., Christian T., and Matthias N.: Face2Face: Real-time face capture and reenactment of RGB videos. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 2387–2395 (2016)

  59. Deepfakes github.: https://github.com/ deepfakes/faceswap. Accessed: 2018-10-29

  60. Faceswap. https://github.com/ MarekKowalski/FaceSwap/. Accessed: 2018-10-29

  61. Thies, J., Zollhöfer, M., Nießner, M.: Deferred neural rendering: image synthesis using neural textures. ACM Trans. Gr. (TOG) 38(4), 1–12 (2019)

    Article  Google Scholar 

  62. Google AI Blog. Contributing data to deepfake detection research. https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html. Accessed: 2019-09-25

  63. Jiang, L., Li, R., Wu, W., Qian, C., Loy, C.C.: Deeperforensics-1.0: A large-scale dataset for real-world face forgery detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2889–2898 (2020)

  64. Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceeding of ICCV, vol. 2. IEEE, 1150–1157 (1999)

  65. Fridrich, A.J., Soukal, B.D., and Lukáš, A.J.: Detection of copymove forgery in digital images. In: Proceedings of Digital Forensic Research Workshop, Citeseer (2003)

  66. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  67. Bashar, M., Noda, K., Ohnishi, N., Mori, K.: Exploring duplicated regions in natural images. In: IEEE Trans. Image Process. (2010)

  68. Wall, M.E., Rechtsteiner. A., Rocha, L.M.: Singular value decomposition and principal component analysis. In: A Practical Approach to Microarray Data Analysis, pp. 91–109. Springer (2003)

  69. Shlens, J.: A tutorial on principal component analysis (2014). arXiv preprint arXiv:1404.1100

  70. Bay, H., Tuytelaars, T., Van Gool, L.S.U.R.F.: Speeded up robust features. In: Proceeding of ECCV, pp 404–417 (2006)

  71. Huang, H., Guo, W., Zhang, Y.: Detection of copy-move forgery in digital images using sift algorithm. In: 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, vol. 2, pp. 272–276. IEEE (2008)

  72. Zhu, Y., Shen, X., Chen, H.: Copy-move forgery detection based on scaled ORB. Multimed. Tools Appl. 75(6), 3221–3233 (2016)

    Article  Google Scholar 

  73. Chen, W., Shi, Y.Q., Su, W.: Image splicing detection using 2D phase congruency and statistical moments of characteristic function. In Security, Steganography, and Watermarking of Multimedia Contents IX, vol. 6505, p. 65050R. International Society for Optics and Photonics (2007)

  74. Johnson, M.K., Farid, H.: Exposing digital forgeries in complex lighting environments. IEEE Trans. Inf. Forensics Secur. 2(3), 450–461 (2007)

    Article  Google Scholar 

  75. Popescu, A.C., Farid, H.: Statistical tools for digital forensics. In: International Workshop on Information Hiding, pp. 128–147. Springer (2004)

  76. Matern, F., Riess, C., and Stamminger, M.: Exploiting visual artifacts to expose DeepFakes and face manipulations. In: Proc. IEEE Winter Applications of Computer Vision Workshops (2019)

  77. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning (2016)

  78. Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: Proc. International Conference on Acoustics, Speech and Signal Processing (2019)

  79. Jung, T., Kim, S., and Kim, K.: DeepVision: Deepfakes Detection Using Human Eye Blinking Pattern, IEEE Access, vol. 8, pp. 83 144–83 154 (2020)

  80. Sabir, E., Cheng, J., Jaiswal, A., AbdAlmageed, W., Masi, I., Natarajan, P.: Recurrent convolutional strategies for face manipulation detection in videos. In: Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2019)

  81. Tolosana, R., Romero-Tapiador, S., Fierrez, J., and Vera-Rodriguez, R.: DeepFakes evolution: analysis of facial regions and fake detection performance (2020). arXiv preprint arXiv:2004.07532

  82. Wang, R., Ma, L., Juefei-Xu, F., Xie, X., Wang, J., and Liu, Y.: FakeSpotter: a simple baseline for spotting AI-synthesized fake faces (2019). arXiv preprint arXiv:1909.06122

  83. Marra, F., Saltori, C., Boato, G., and Verdoliva, L.: Incremental learning for the detection and classification of GAN-generated images. In: Proc. IEEE International Workshop on Information Forensics and Security (2019)

  84. Zhang, X., Karaman, S., and Chang, S.: Detecting and simulating artifacts in GAN fake images. In: Proc. IEEE International Workshop on Information Forensics and Security (2019)

  85. Rathgeb, C., Botaljov, A., Stockhardt, F., Isadskiy, S., Debiasi, L., Uhl, A., Busch, C.: PRNU-based Detection of Facial Retouching. IET Biometrics (2020)

  86. Nguyen, H., Fang, F., Yamagishi, J., and Echizen, I.: Multi-task learning for detecting and segmenting manipulated facial images and videos (2019). arXiv preprint arXiv:1906.06876

  87. Amerini, I., Galteri, L., Caldelli, R., and Bimbo, A.: Deepfake video detection through optical flow based CNN

  88. Sun, D., Yang, X., Liu, M.Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost Volume. In: Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018)

  89. Sabir, E., Cheng, J., Jaiswal, A., AbdAlmageed, W., Masi, I., Natarajan, P.: Recurrent convolutional strategies for face manipulation detection in videos. In: Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2019)

  90. Dang, H., Liu, F., Stehouwer, J., Liu, X., Jain, A.: On the detection of digital face manipulation. In: Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)

  91. Vinolin, V., Sucharitha, M.: Dual adaptive deep convolutional neural network for video forgery detection in 3D lighting environment. Vis. Comput. pp. 1–22 (2020)

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

    Article  Google Scholar 

  93. Zhao, X., Wang, S., Li, S., Li, J.: Passive image splicing detection by a 2-D noncausal Markov model. IEEE Trans. Circuits Syst. Video Technol. 25(2), 185–199 (2015)

    Article  Google Scholar 

  94. Li, B., Ng, T., Li, X., Tan, S.: Revealing the trace of high-quality JPEG compression through quantization noise analysis. IEEE Trans. Inf. Forensics Secur. 10(3), 558–573 (2015)

    Article  Google Scholar 

  95. Yin, T., Yang, G., Li, L., Zhang, D.: Detecting seam carving based image resizing. Comput. Secur. 55, 130–141 (2015)

    Article  Google Scholar 

  96. Zhang, Y., Goh, J., Lei, L., Thing, V.: Image region forgery detection: a deep learning approach. Singap. Cyber-Secur. Conf. 14, 1–11 (2016)

    Google Scholar 

  97. Yu, J., Zhan, Y., Xiangui, Yang J., KB.: A multi-purpose image counter-anti-forensic method using convolutional neural networks. In: International Workshop on Digital Watermarking, pp. 3–15 (2017)

  98. Lee, J.-C., Chang, C.-P., Chen, W.-K.: Detection of copy-move image forgery using histogram of orientated gradients. Inf Sci (NY) 321, 250–262 (2015)

    Article  Google Scholar 

  99. Cozzolino, D., Poggi, G., Verdoliva, L.: Efficient dense-field copy-move forgery detection. IEEE Trans. Inf. Forensics Secur. 10(11), 2284–2297 (2015)

    Article  Google Scholar 

  100. 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 

  101. Tuba, V., Jovanovic, R., Tuba, M.: Digital image forgery detection based on shadow HSV inconsistency. In: 5th International Symposium on Digital Forensic and Security (ISDFS) (2017)

  102. Bahrami, K., Kot, A.C., Li, L., Li, H.: Blurred image splicing localization by exposing blur type inconsistency. IEEE Trans. Inf. Forensics Secur. 10(5), 999–1009 (2015)

    Article  Google Scholar 

  103. Li, H., Luo, W., Qiu, X., Huang, J.: Image forgery localization via integrating tampering possibility maps. IEEE Trans. Inf. Forensics Secur. 12(5), 1240–1252 (2017)

    Article  Google Scholar 

  104. Abdul Warif, N.B., Abdul Wahab, A.W., Idna Idris, M.Y., Fazidah Othman, R.S.: SIFT-Symmetry: a robust detection method for copy-move forgery with reflection attack. J. Vis. Commun. Image Represent. 46, 219–232 (2017)

    Article  Google Scholar 

  105. Banerjee, A., Das, N., Santosh, K.C.: Weber local descriptor for image analysis and recognition: a survey. Vis. Comput. pp. 1–23 (2020)

  106. Bowling, M., Veloso, M.: Multiagent learning using a variable learning rate. Artif. Intell. 136(2), 215–250 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  107. Li, Y., Xin Y., Pu, S., Honggang, Q., and Siwei, L.: A new dataset for deepfake forensics. arXiv preprint, Celeb-df (2019)

  108. 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 

  109. 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)

  110. 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, ACM (2016)

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

  112. 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, IEEE (2016)

  113. 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, IEEE (2017)

  114. Bondi, L., Lameri, S., D. Güera, P. Bestagini, E. J. Delp, and S. Tubaro,: 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. IEEE (2017)

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

    Article  Google Scholar 

  116. 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), pp. 168–184 (2018)

  117. 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)

  118. Wang, X., Wang, H., Niu, S., and Zhang, J.: Detection and localization of image forgeries using improved mask regional convolutional neural network (2019)

  119. Wang, Sheng-Yu., et al.: Detecting photoshopped faces by scripting photoshop. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2019)

  120. Muzaffer, G., Ulutas, G.: A new deep learning-based method to detection of copy-move forgery in digital images. In: 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT). IEEE (2019)

  121. Kuznetsov, A.: Digital image forgery detection using deep learning approach. J. Phys. Conf. Ser. Vol. 1368. No. 3. IOP Publishing (2019)

  122. Marra, F., Gragnaniello, D., Verdoliva, L., Poggi, G.: A full-image full-resolution end-to-end-trainable CNN framework for image forgery detection. IEEE Access 8, 133488–133502 (2020). https://doi.org/10.1109/ACCESS.2020.3009877

    Article  Google Scholar 

  123. Hossein-Nejad, Z., Nasri, M.: Clustered redundant keypoint elimination method for image mosaicing using a new Gaussian-weighted blending algorithm. Vis. Comput. 1–17 (2021)

  124. Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2017)

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Tyagi, S., Yadav, D. A detailed analysis of image and video forgery detection techniques. Vis Comput 39, 813–833 (2023). https://doi.org/10.1007/s00371-021-02347-4

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