Deep fake Detection

References

[1] FaceForensics detection paper: FaceForensics++: Learning to Detect Manipulated Facial Images – 2019 paper from IEEEexplore.

[2] Irene Amerini, Lamberto Ballan, Roberto Caldelli, Alberto Del Bimbo, and Giuseppe Serra A SIFT-based forensic method for copy-move attack detection and transformation recovery IEEE Transactions on Information Forensics and Security, Mar. 2011.

[3] P. Bestagini et al. Local tampering detection in video sequences. IEEE International Workshop on Multimedia Signal Processing, pages 488– 493, Sept. 2013. Pula, Italy

[4] Grigory Antipov, Moez Baccouche, and Jean- Luc Dugelay. Face aging with conditional generative adversarial networks. In IEEE International Conference on Image Processing, 2017.

[5] Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes. In IEEE Computer Vision and Pattern Recognition, 2017

[6] Francois Chollet. Xception: Deep Learning with Depthwise Separable Convolutions. In IEEE Conference on Computer Vision and Pattern Recognition, 2017

[7] Paul Upchurch, Jacob Gardner, Geoff Pleiss, Robert Pless, Noah Snavely, Kavita Bala, and Kilian Weinberger. Deep feature interpolation for image content changes. In IEEE Conference on Computer Vision and Pattern Recognition, 2017.

[8] Luca D’Amiano, Davide Cozzolino, Giovanni Poggi, and Luisa Verdoliva. A PatchMatch- based Dense-field Algorithm for Video Copy- Move Detection and Localization. IEEE Transactions on Circuits and Systems for Video Technology, in press, 2018

[9] Yuezun Li, Ming-Ching Chang, and Siwei Lyu. In Ictu Oculi: Exposing AI Created Fake Videos by Detecting Eye Blinking. In IEEE WIFS, 2018

[10] Justus Thies, Michael Zollhofer, Marc Stamminger, Christian Theobalt, and Matthias Nießner. FaceVR: Real-Time Gaze-Aware Facial Reenactment in Virtual Reality. ACM Transactions on Graphics (TOG), 2018.

[11] Darius Afchar, Vincent Nozick, Junichi Yamagishi, and Isao Echizen. Mesonet: a compact facial video forgery detection network. arXiv preprint arXiv:1809.00888, 2018.

[12] Justus Thies, Michael Zollhofer, and Matthias Nießner. Deferred neural rendering: Image synthesis using neural textures. ACM Transactions on Graphics 2019 (TOG), 2019.

@article{1702830,
      author = {K. Rakesh, M. Veera Mani Kanta, M. Mahesh Reddy, M. Tejaggna, Ms. R. Tejaswini},
      title = {Deep fake Detection},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {5},
      number = {1},
      pages = {182-187},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1702830.pdf},
      abstract = {The expeditious progress in facial image generation and exploitation has now come to a point where it raises serious concerns to the social and political society. This leads to the creation of fake information and new which ultimately results in loss of trust in digital content. We have developed a detection model using convolution neural network (CNN) for face detection and Recurrent neural network (RNN) for video classification. Even though this technology is remarkable it leads to social and political concerns. So far, with the help of released tools for the generation of deep fake videos have been widely used to create fake celebrity videos or revenge porn and fake political speeches, etc. Governmental entities are already looking into the issue of these fake videos which are likely to create political tensions. so, it is essential to have a tool for detecting these fake videos. (We need AI to fight an AI)},
      keywords = {Convolution Neural Networks (CNN), LSTM, RNN.},
      month = {July},
  }