Keywords:-
Article Content:-
Abstract
Scrutinises many methods that are employed by numerous scholars to identify Deep phoney films. We offer a method for spotting identity changes, such face swapping, in a single photograph. Deep Fake and other facial swapping methods alter the face region in an effort to replicate the appearance of the context while changing only the face. We show that the two regions differ as a result of this mode of operation. These variations raise red flags for manipulative activity. Our method makes use of a pair of networks: one for face identification, that takes into account face region included within accurate semantic segmentation & specifies gender and age, and another for context recognition, which takes into account face's surrounding context. Recognizing the Meaning of Emoticons, we provide a strategy which makes use of recognition signals from our 2 networks to pinpoint these deviations, so offering an enhanced, non-traditional detection signal. We propose a deep fake-detection method based on an organ-level transformer model for extracting deep fake characteristics. By giving less weight to identification of deformed or unclear organs, we give priority to detection of obvious and intact organs. Our system can also detect subtle alterations to facial expressions and details, as well as heavily tainted, digitally-generated phony images. So-called "micro expressions" are fast, fleeting facial changes. This kind of unrestrained display of emotion is a window into a person's true feelings. examined the seven most common micro expressions seen in human faces: happiness, sadness, anger, disgust, contempt, fear, & surprise.
References:-
References
Asad Malik1 , (Member, IEEE), Minoru Kuribayashi (Senior Member, IEEE), Sani M Abdullahi 3 ,(Member, IEEE), and Ahmed NeyazKhan 4 , (Member, IEEE) ,“deepfake Detection for Human Face Images and Videos: A Survey” (2022)
J. Thies, M. Zollhofer, M. Stamminger, C. Theobalt, and M. Nießner, “Face2face: Real-time face capture and reenactment of RGB videos,” in Proc. Conf. Comput. Vis. Pattern Recognit., pp. 2387–2395. (2016)
J. Thies, M. Zollhofer, and M. Nießner, “Deferred neu-ral rendering: € Image synthesis using neural tex-tures,” arXiv:1904.12356. (2019)
Deepfakes, “Deepfakes.” Accessed: Nov. 15. [Online]. Available: https://github.com/deepfakes/faceswap (2019)
Md Shohel Rana, (Member, IEEE), Mohammad Nur Nobi, (Member, IEEE), Beddhu Murali , And Andrew H. Sung ,(Member, IEEE), "Deepfake Detection: A Systematic Literature Review"(2022)A. Rossler, D. Cozzolino, L.Verdoliva, C. Riess, J. Thies, and M. € Nießner, “Faceforensics++: Learning to detect ma-nipulate facial images,” arXiv:1901.08971. (2019)
FaceSwap, “FaceSwap.” Accessed: Nov. 15, 2019. [Online]. Available: https://github.com/MarekKowalski/FaceSwap/
Y. Nirkin, Y. Keller, and T. Hassner, “FSGAN: Subject agnostic face swapping and reenactment,” in Proc. Int. Conf. Comput.Vis., pp. 7184–7193. (2019)
D. Afchar, V. Nozick, J. Yamagishi, and I. Echizen, “MesoNet: A compact facial video forgery detection network,” in Proc. Int.Workshop Inf. Forensics Secur., (2018), pp. 1–7.
Deng Pan; Lixian Sun; Rui Wang; Xingjian Zhang; Richard O.Sinnott , "Deepfake Detection through Deep Learn-ing"https://ieeexplore.ieee.org/document/9302547(2020)
D. Cozzolino, G. Poggi, and L. Verdoliva, “Recasting residual-based local descriptors as convolutional neu-ral networks: an application to image forgery detec-tion,” in Int. Workshop on Information Hiding and Multimedia Security. ACM, pp.159–164.(2017)
J. Fridrich and J. Kodovsky, “Rich models for ste-ganalysis of digital images,” Trans. on Inform. Foren-sics and Security,vol. 7, no. 3, pp. 868–882, (2012).
N. Rahmouni, V. Nozick, J. Yamagishi, and I. Echizen,
“Distinguishing computer graphics from natural imag-es using convolution neural networks,” in Int. Work-shop onInformation Forensics and Security. IEEE, (2017).
N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Na-yar, “Attribute and simile classifiers for face verifica-tion,” in Proc.Conf. Comput. Vision Pattern Recogni-tion. IEEE, pp. 365–372. (2009)
Y. Nirkin, I. Masi, A. T. Tuan, T. Hassner, and G. Me-dioni, “On face segmentation, face swapping, and face perception,” in Int. Conf. on Automatic Face and Gesture Recognition. IEEE, pp. 98–105. (2018)
Y. Li, M.-C. Chang, and S. Lyu, “In ictu oculi: Expos-ing AI generated fake face videos by detecting eye blinking,” arXiv preprint arXiv:1806.02877, (2018). V. Blanz, S. Romdhani, and T. Vetter, “Face identifica-tion across different poses and illuminations with a 3d morphable model,” in Int. Conf. on Automatic Face and Gesture Recognition, pp. 192–197. (2002)
V. Blanz and T. Vetter, “Face recognition based on fit-ting a3d morphable model,” Trans. Pattern Anal. Mach. Intell., vol. 25, no.9, pp. 1063–1074, (2003).
Y. Li, X. Yang, P. Sun, H. Qi, and S. Lyu, “Celeb-DF: A new dataset for deepfake forensics,” arXiv preprint
arXiv:1909.12962, (2019).
B. Dolhansky, R. Howes, B. Pflaum, N. Baram, and C. C. Ferrer, “The deepfake detection challenge (DFDC) preview dataset,” arXiv preprint arXiv:1910.08854, (2019).
D. Bitouk, N. Kumar, S. Dhillon, P. Belhumeur, and S. K. Nayar, “Face swapping: automatically replacing faces in photographs,” ACM Trans. on Graphics, vol. 27, no. 3, p. 39, (2008).
V. Blanz, K. Scherbaum, T. Vetter, and H.-P. Seidel,
“Exchanging faces in images,” Comput. Graphics Fo-rum, vol. 23, no. 3, pp. 669–676, (2004).
Y. Lin, S. Wang, Q. Lin, and F. Tang, “Face swapping under large pose variations: A 3D model based ap-proach,” in Int. Conf. on Multimedia and Expo. IEEE, pp. 333–338. (2012)
S. Mosaddegh, L. Simon, and F. Jurie, “Photorealistic face deidentification by aggregating donors face components,” in Asian Conf. Comput. Vision. Springer, pp. 159–174.(2014)
Y. Wu, W. AbdAlmageed, and P. Natarajan, “Man-Tra-Net: Manipulation tracing network for detection and localization of image forgeries with anomalous features,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9543–9552.(2019)
P. Korshunov and S. Marcel, “Speaker inconsistency detection in tampered video,” in European Signal Pro-cessing Conf. IEEE pp. 2375–2379. (2018)
V. Blanz, S. Romdhani, and T. Vetter, “Face identifi-cation across different poses and illuminations with a 3d morphable model,” in Int. Conf. on Automatic Face and Gesture Recognition, pp. 192–197. (2002)
Y. Li and S. Lyu, “Exposing deepfake videos by de-tecting face warping artifacts,” arXiv preprint arXiv:1811.00656, (2018).
W. Quan, K. Wang, D.-M. Yan, and X. Zhang, “Distin-guishing between natural and computer-generated images using convolutional neural networks,” Trans. on Inform. Forensics and Security, vol. 13, no. 11, pp. 2772–2787, (2018).
A. Rossler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and ¨ M. Nießner, “Faceforensics: A large-scale video dataset for forgery detection in human faces,” arXiv preprint arXiv:1803.09179,( 2018).
E. Sabir, J. Cheng, A. Jaiswal, W. AbdAlmageed, I. Masi, and P. Natarajan, “Recurrent convolutional strategies for face manipulation detection in videos,” Interfaces (GUI), vol. 3, p.1, (2019).
DeepFake Detection Based on Discrepancies Between Faces and their Context Yuval Nirkin, Lior Wolf, Yosi Keller, and Tal Hassner
Google AI, “Contributing data to deepfake detection research.” [Online]. Available: https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html (2019)
A. Rossler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. € Nießner, “Faceforensics++: Learning to detect manipulate facial images,” arXiv:1901.08971. (2019)
B. Bayar and M. C. Stamm, “A deep learning ap-proach to universal image manipulation detection using a new convolutional layer,” in Proc. Int. Work-shop Inf. Hiding Multimedia Secur., 2016, pp. 5–10. [10] D. Cozzolino, G. Poggi, and L. Verdoliva, “Re-casting residualbased local descriptors as convolu-tional neural networks:An application to image for-gery detection,” in Proc. Int. Workshop Inf. Hiding Multimedia Secur., pp. 159–164. (2017) Deng Pan; Lixian Sun; Rui Wang; Xingjian Zhang; Richard O.Sinnott , "Deepfake Detection through Deep Learning "https://ieeexplore.ieee.org/document/9302547(2020)
Md. Shohel Rana; Beddhu Murali; Andrew H. Sung ,
"Deepfake Detection Using Machine Learning Algo-rithms"https://ieeexplore.ieee.org/document/9790940(2021)
Sonia Singla Age and Gender Detection Using Deep
Learning,(2022)
Mr. Aditya Kulkarni, Mr. Parth Joshi, Mr. Shaunak Sindgi, Mr. Shreyas Rakshasbhuvankar, Mr. Vivek Kumar, Prof. Madhavi Dachawar”Detection of Gen-der and Age using Machine Learning”,(2022)
Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms Xianye Ben, Member, IEEE, Yi Ren, Junping Zhang, Member, IEEE, Su-Jing Wang,Senior Member, IEEE, Kidiyo Kpalma, Weixiao Meng, Senior Member,IEEE, Yong-Jin Liu, Senior Member, IEEE(2022)