Deep Learning Based Web Data Classification Techniques for Forensic Analysis: An Overview
Keywords:DeepFake Detection, Deep Learning, Forensic, Web Data
The rapid advancement of artificial intelligence (AI), machine learning (ML), and deep learning (DL) over the course of the previous several decades has led to the development of a variety of novel methodologies and tools for the manipulation of audiovisual. Even though technological advancement has been utilized for the most part in acceptable uses, like being used for entertaining and educational purposes, etc., fraudulent people have nonetheless found ways to abuse it for illegal or evil objectives. For instance, high-quality and convincingly authentic fake videos, photos, or audio recordings have been produced with the goals of disseminating false data and disinformation, sowing the seeds of political dissension and hatred, and even intimidating and blackmailing individuals. Deepfake is a relatively new term that refers to videos that have been modified but nevertheless maintain a high level of quality and realism. This application's intuitive qualities have contributed to a widespread rise in its appeal among the general public, and it is currently being utilized in a variety of fields, including fraudulent transactions, online criminal activity, politics, and possibly military operations. Therefore, it is of the utmost need to establish a variety of methods for detection that are capable of doing away with this kind of forgery and putting up an entirely novel approach in audio as well as video forensics. In this research work, the numerous detection strategies are presented that have been currently under investigation in the field of Deepfake research. So as to serve as the backbone for the creation of a new technique that would be more compressible and effective in identifying the presence of Deepfakes, this will be necessary. Also, a research investigation was conducted to compare different methods that are used in conventional methods with those that are used in state-of-the-art approaches. The investigation came to the conclusion that the majority of the methodologies that are used in conventional approaches are processes that take a lot of time, require skill and understanding of the technology for the individual attempting to use them, and so on. An introduction is given to the technological issues posed by DeepFake detection, as well as the methods researchers use to devise potential solutions to this issue. The benefits and disadvantages for every sort of solution, in addition to any possible hazards and disadvantages, are dissected and analyzed here. Despite this advancement, there are still a variety of significant issues that need to be fixed before present DeepFake detection approaches can be considered fully viable. Several of these issues are brought to light, and a discussion of the investigation prospects available in this field follows.
Chesney R, Citron DK (2019) Deep Fakes: a looming challenge for privacy, democracy, and national security. In: 107 California Law Review (2019, Forthcoming); U of Texas Law, Public Law Research Paper No. 692; U of Maryland Legal Studies Research Paper No. 2018-21
Li Y, Chang M-C, Lyu S (2018) In Ictu Oculi: exposing AI generated fake face videos by detecting eye blinking. In: IEEE international workshop on information forensics and security (WIFS)
Bitouk D, Kumar N, Dhillon S, Belhumeur P, Nayar SK (2008) Face swapping: automatically replacing faces in photographs. ACM Trans Graph (TOG)
Dale K, Sunkavalli K, Johnson MK, Vlasic D, Matusik W, Pfister H (2011) Video face replacement. ACM Trans Graph (TOG)
Suwajanakorn S, Seitz SM, Kemelmacher-Shlizerman I (2015) What makes tom hanks look like tom hanks. In: ICCV
Suwajanakorn S, Seitz SM, Kemelmachershlizerman I (2017) Synthesizing obama: learning lip sync from audio. ACM Trans Graph 36(4):95
Thies J, Zollhofer M, Stamminger M, Theobalt C, Niessner M (2016) Face2Face: real-time face capture and reenactment of rgb videos. In: IEEE conference on computer vision and pattern recognition (CVPR)
Liu M-Y, Breuel T, Kautz J (2017) Unsupervised image-to-image translation networks. In: NeurIPS
Kazemi V, Sullivan J (2014) One millisecond face alignment with an ensemble of regression trees. In: CVPR
Afchar D, Nozick V, Yamagishi J, Echizen I (2018) Mesonet: a compact facial video forgery detection network. In: WIFS
Güera D, Delp EJ (2018b) Deepfake video detection using recurrent neural networks. In: AVSS
McCloskey S, Albright M (2018) Detecting gan-generated imagery using color cues. arXiv:1812.08247
Li Y, Lyu S (2019) Exposing deepfake videos by detecting face warping artifacts. In: IEEE conference on computer vision and pattern recognition workshops (CVPRW)
Li L, Bao J, Zhang T, Yang H, Chen D, Wen F, Guo B (2019a) Face x-ray for more general face forgery detection. arXiv:1912.13458
Frank J, Eisenhofer T, Schönherr L, Fischer A, Kolossa D, Holz T (2020) Leveraging frequency analysis for deep fake image recognition. arXiv:2003.08685
Durall R, Keuper M, Keuper J (2020) Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions. arXiv:2003.01826
Guarnera L, Battiato S, Giudice O (2020) Deepfake detection by analyzing convolutional traces. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops
Yang X, Li Y, Lyu S (2019) Exposing deep fakes using inconsistent head poses. In: ICASSP
Matern F, Riess C, Stamminger M (2019) Exploiting visual artifacts to expose deepfakes and face manipulations. In: IEEE winter applications of computer vision workshops (WACVW)
Ciftci UA, Demir I, Yin L (2020) How do the hearts of deep fakes beat? Deep fake source detection via interpreting residuals with biological signals. In: IEEE/IAPR international joint conference on biometrics (IJCB)
Hu S, Li Y, Lyu S (2009) Exposing GAN-generated faces using inconsistent corneal specular highlights. arXiv:11924:2020
Sabir E, Cheng J, Jaiswal A, AbdAlmageed W, Masi I, Natarajan P (2019) Recurrent convolutional strategies for face manipulation detection in videos. Interfaces (GUI) 3:1
Rössler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nießner M (2019) FaceForensics++: learning to detect manipulated facial images. In: ICCV
Nguyen HH, Yamagishi J, Echizen I (2019b) Capsule-forensics: using capsule networks to detect forged images and videos. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2307–2311
Nataraj L, Mohammed TM, Manjunath BS, Chandrasekaran S, Flenner A, Bappy JH, RoyChowdhury AK (2019) Detecting gan generated fake images using co-occurrence matrices. Electron Imag (2019)5:532–1
Do N-T, Na I-S, Kim S-H (2018) Forensics face detection from gans using convolutional neural network
Tan M, Le Q (2019) EfficientNet: rethinking model scaling for convolutional neural networks. In: Chaudhuri K, Salakhutdinov R (eds) Proceedings of the 36th international conference on machine learning, vol 97 of Proceedings of machine learning research, Long Beach, California, USA, 09–15 Jun 2019. PMLR, pp 6105–6114
Amerini I, Galteri L, Caldelli R, Del Bimbo A (2019) Deepfake video detection through optical flow based cnn. In: Proceedings of the IEEE international conference on computer vision workshops, pp 0-0
Koopman M, Rodriguez AM, Geradts Z (2018) Detection of deepfake video manipulation. In: The 20th Irish machine vision and image processing conference (IMVIP), pp 133–136
Huang Y, Juefeixu F, Wang R, Xie X, Ma L, Li J, Miao W, Liu Y, Pu G (2020) Fakelocator: robust localization of gan-based face manipulations via semantic segmentation networks with bells and whistles. Computer vision and pattern recognition. arXiv:2001.09598
Korshunov P, Marcel S (2018) Deepfakes: a new threat to face recognition? Assessment and detection. arXiv:1812.08685
Dolhansky B, Howes R, Pflaum B, Baram N, Ferrer CC (2019) The deepfake detection challenge (DFDC) preview dataset. arXiv:1910.08854
Li Y, Sun P, Qi H, Lyu S (2020) Celeb-DF: a Large-scale challenging dataset for DeepFake forensics. In: IEEE conference on computer vision and patten recognition (CVPR), Seattle, WA, United States
Jiang L, Wu W, Li R, Qian C, Loy CC (2020) Deeperforensics-1.0: a large-scale dataset for real-world face forgery detection. arXiv:2001.0302
Stehouwer J, Dang H, Liu F, Liu X, Jain AK (2019) On the detection of digital face manipulation. Computer vision and pattern recognition. arXiv:1910.01717
Raahat Devender Singh, Naveen Aggarwal,” Video content authentication techniques: a comprehensive survey”, Springer, Multimedia Systems, pp. 211- 240, 2018.
David G’uera Edward J. Delp,” Deepfake Video Detection Using Recurrent Neural Networks”, Video and Image Processing Laboratory (VIPER), Purdue University,2018.
Ekraam Sabir, Jiaxin Cheng, Ayush Jaiswal, Wael AbdAlmageed, Iacopo Masi, Prem Natarajan,” Recurrent Convolutional Strategies for Face Manipulation Detection in Videos”, In proceeding of the IEEE Xplore Final Publication, pp. 80-87, 2018.
Xinyi Ding, Zohreh Razieiy, Eric C, Larson, Eli V, Olinick, Paul Krueger, Michael Hahsler,” Swapped Face Detection using Deep Learning and Subjective Assessment”, Research Gate, pp. 1-9, 2019.
Peng Zhou, Xintong Han, Vlad I. Morariu Larry S. Davis,” Two-Stream Neural Networks for Tampered Face Detection”, IEEE Conference on Computer Vision and Pattern Recognition, 2019
Chih-Chung Hsu, Yi-Xiu Zhuang, and Chia-Yen Lee,” Deep Fake Image Detection based on Pairwise Learning”, MDPI, Applied Science,2020, doi:10.3390/app10010370.
Fang Liu, Licheng Jiao, Fellow, IEEE, and Xu Tang, Member” Task-Oriented GAN for PolSAR Image Classification and Clustering”, IEEE Transactions On Neural Networks and Learning Systems, Volume 30, Issue 9, 2019.
Jawadul H. Bappy, Cody Simons, Lakshmanan Nataraj, B.S. Manjunath, and Amit K. Roy-Chowdhury,” Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries”, IEEE Transaction on Image Processing, Volume: 28 , Issue: 7 ,pp. 1-14, 2019.
Pavel Korshunov, S´ebastien Marcel,” Speaker Inconsistency Detection in Tampered Video”, 26th European Signal Processing Conference (EUSIPCO), 2018, ISBN 978-90-827970-1-5.
Xinsheng Xuan, Bo Peng, Wei Wang and Jing Dong,” On the Generalization of GAN Image Forensics”, Computer Vision and Pattern Recognition, Cornell University, Volume 1, pp. 1-8, 2019.
Yuezun Li, Siwei Lyu,” Exposing DeepFake Videos by Detecting Face Warping Artifacts”, In Proceedings of the IEEE Xplore Final Publication, pp. 46- 52, 2019.
Huy H. Nguyen, Junichi Yamagishi, and Isao Echizen,” Capsule-Forensics: Using Capsule Networks to detect Forged Images and Videos”, ICASSP, pp. 2307 – 2311, 2019
Li, Y., Chang, M. C., and Lyu, S, “Exposing AI created fake videos by detecting eye blinking”, In IEEE International Workshop on Information Forensics and Security (WIFS) (pp. 1-7). 2018.
A.M. Rodriguez, Z. Geradts,” Detection of Deepfake Video Manipulation”, In Proceedings of the 20th Irish Machine Vision and Image Processing conference, Belfast, Northern Ireland, pp. 133-136, 2018, ISBN 978-0-9934207-3-3.
Steven Fernandes, Sunny Raj, Rickard Ewetz, Jodh Singh Pannu, Sumit Kumar Jha, Eddy Ortiz, Iustina Vintila, Margaret Salte,” Detecting deepfake videos using attribution-based confidence metric”, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 308-309), 2020.
Carlini N, Farid H (2020) Evading deepfake-image detectors with white- and black-box attacks. arXiv:2004.00622
Gandhi A, Jain S (2020) Adversarial perturbations fool deepfake detectors. arXiv:2003.10596
Neekhara P (2020) Adversarial deepfakes: evaluating vulnerability of deepfake detectors to adver-sarial examples. arXiv:2002.12749
Ibrahim, S. S. ., & Ravi, G. . (2023). Deep learning based Brain Tumour Classification based on Recursive Sigmoid Neural Network based on Multi-Scale Neural Segmentation. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 77–86. https://doi.org/10.17762/ijritcc.v11i2s.6031
Mr. Kaustubh Patil, Promod Kakade. (2014). Self-Sustained Debacle Repression Using Zig-Bee Communication. International Journal of New Practices in Management and Engineering, 3(04), 05 - 10. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/32
Anand, R., Khan, B., Nassa, V.K., Pandey, D., Dhabliya, D., Pandey, B.K., Dadheech, P. Hybrid convolutional neural network (CNN) for Kennedy Space Center hyperspectral image (2023) Aerospace Systems, 6 (1), pp. 71-78.
How to Cite
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.