Open Access
ITM Web Conf.
Volume 32, 2020
International Conference on Automation, Computing and Communication 2020 (ICACC-2020)
Article Number 03005
Number of page(s) 6
Section Computing
Published online 29 July 2020
  1. Y. Guo, X. Cao, W. Zhang and R. Wang, “Fake Colorized Image Detection,” in IEEE Transactions on Information Forensics and Security, Vol. 13, no. 8, pp. 1932-1944, August 2018. [Google Scholar]
  2. F. Marra, D. Gragnaniello, D. Cozzolino and L. Verdo-liva, “Detection of GAN-Generated Fake Images over Social Networks,” 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), Miami, FL, pp. 384-389, 2018. [Google Scholar]
  3. L. Zhuo, S. Tan, J. Zeng and B. Lit, “Fake Colorized Image Detection with Channel-wise Convolution based Deep-learning Framework,” 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Honolulu, HI, USA, pp. 733-736, 2018. [Google Scholar]
  4. T. Pomari, G. Ruppert, E. Rezende, A. Rocha and T. Carvalho, “Image Splicing Detection Through Illumination Inconsistencies and Deep Learning,” 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, pp. 3788-3792, 2018. [Google Scholar]
  5. V. Mall, A.K. Roy and S.K. Mitra, “Digital image tampering detection and localization using singular value decomposition technique,” 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), Jodhpur, pp. 1-4, 2013. [Google Scholar]
  6. T. Edmunds and A. Caplier, “Fake face detection based on radiometric distortions,” 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA), Oulu, pp. 1-6, 2016. [Google Scholar]
  7. C. Hsu, C. Lee and Y. Zhuang, “Learning to Detect Fake Face Images in the Wild,” 2018 International Symposium on Computer, Consumer and Control (IS3C), Taichung, Taiwan, pp. 388-391, 2018. [Google Scholar]
  8. Tariq, Shahroz Lee, Sangyup Kim, Hoyoung Shin, Youjin Woo, Simon. (2018). Detecting Both Machine and Human Created Fake Face Images In the Wild. 81-87. 10.1145/3267357.3267367. [Google Scholar]
  9. Kumar, Manoj Srivastava, Sangeet. (2017). Image forgery detection based on physics and pixels: a study. Australian Journal of Forensic Sciences. 51. 1-16. 10.1080/00450618.2017.1356868. [Google Scholar]
  10. J. Kim, S. Han and S. S. Woo, “Classifying Genuine Face images from Disguised Face Images,” 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 6248-6250. [Google Scholar]

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