A Systematic Implementation of Deepfake Multimedia Video Generation and Detection using Deep Learning
0
조회
0
다운로드
0
인용
0
참고문헌
초록
In the following years, technology has progressed in so many ways that it has provided the cyber society with a resource that only computers can excel at, such as the art of counterfeit of media, which was before unavailable. Deepfakes are a term used to describe this kind of deception. The majority of well-documented Deep Fakes are produced using Generative Adversarial Network (GAN) Models, which are essentially two distinct Machine Learning Models that perform the roles of attack and defence. These models create and identify deepfakes until they reach a point where the morphing no longer detects the deepfakes anymore. Using this algorithm/model, it is possible to discover and create new media that has a similar demographic to the training set, resulting in the development of the ideal Deep Fake media. Because the alterations are carried out utilising advanced characteristics, they cannot be seen with the human eye. However, it is completely feasible to develop an algorithm that can automatically identify this kind of tampering carried out via the internet. This not only enables us to broaden the scope of our search beyond a single media item, but also beyond a large library of mixed media. The more it learns, the better it becomes as artificial intelligence takes over in full force with automation. In order to create better deep fakes, new models are being developed all the time, making it more difficult to distinguish between genuine and morphing material.
Abstract
In the following years, technology has progressed in so many ways that it has provided the cyber society with a resource that only computers can excel at, such as the art of counterfeit of media, which was before unavailable. Deepfakes are a term used to describe this kind of deception. The majority of well-documented Deep Fakes are produced using Generative Adversarial Network (GAN) Models, which are essentially two distinct Machine Learning Models that perform the roles of attack and defence. These models create and identify deepfakes until they reach a point where the morphing no longer detects the deepfakes anymore. Using this algorithm/model, it is possible to discover and create new media that has a similar demographic to the training set, resulting in the development of the ideal Deep Fake media. Because the alterations are carried out utilising advanced characteristics, they cannot be seen with the human eye. However, it is completely feasible to develop an algorithm that can automatically identify this kind of tampering carried out via the internet. This not only enables us to broaden the scope of our search beyond a single media item, but also beyond a large library of mixed media. The more it learns, the better it becomes as artificial intelligence takes over in full force with automation. In order to create better deep fakes, new models are being developed all the time, making it more difficult to distinguish between genuine and morphing material.
참고문헌 (46)
- "Deep Vision: Deepfakes Detection Using Human Eye Blinking Pattern"[URL]
- Eali Stephen Neal Joshua, "3D CNN with Visual Insights for Early Detection of Lung Cancer Using Gradient-Weighted Class Activation", Journal of Healthcare Engineering, 2021, pp.11~ (2021)[DOI]
- Joshua, E. S. N., "An extensive review on lung cancer detection using machine learning techniques : A systematic study", Revue d'Intelligence Artificielle, 34(3), pp.351~359 (2020)[DOI]
- Park, T, "Semantic Image synthesis with spatially-adaptive normalization", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)
- "DeepFaceLab:Explained And Usage Tutorial"[URL]
- Jafar, M. T, "Forensics and analysis of deepfake videos", The 11th International Conference on Information and Communication Systems (ICICS), pp.053~058 (2020), IEEE
- "Creating a data set and a challenge for deepfakes" (2019)[URL]
- Rossler, A., "FaceForensics: A large-scale video dataset for forgery detection in human faces", ~ (2018)
- Choi, Y., "StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
- lang, C., "Defending against GAN-based deepfake attacks via transformation-aware adversarial faces", 4~ (2020)
- Kaliyar, R. K., "Deepfake : improving fake news detection using tensor decomposition based deep neural network", Journal of Supercomputing (2020)[DOI]
- "The newest AI-enabled weapon: Deep-Faking photos of the earth" (2019)[URL]
- "Deep fakes:AI-manipulated media will be weaponised to trick military" (2019)[URL]
- "The best (and scariest) examples of AI-enabled deepfakes" (2019)[URL]
- Zakharov, E., "Few-shot adversarial learning of realistic neural talking head models", arXiv preprint arXiv:1905.08233, ~ (2019)
- "A voice deepfake was used to scam a CEO out of $243,000" (2019)[URL]
- Deep Learning for Deepfakes Creation and Detection: A Survey, "Nguyen, Thanh", ~ (2019)
- Mitra, A, "A Machine Learning Based Approach for Deepfake Detection in Social Media Through Key Video Frame Extraction", SN COMPUT. SCI, 2(98), pp.98~ (2021)[DOI]
- S. Lyu, "Deepfake Detection: Current Challenges and Next Steps", 2020 IEEE International Conference on Multimedia &Expo Workshops (ICMEW), pp.1~6 (2020)
- Maras M-H, Alexandrou A, "Determining authenticity of video evidence in the age of artificial intelligence and in the wake of Deepfake videos", The International Journal of Evidence & Proof, 23(3), pp.255~262 (2019)[DOI]
- Sawinder Kaur, "Deepfakes:temporal sequential analysis to detect face-swapped video clips using convolutional long short-term memory", Journal of Electronic Imaging, 29(3), pp.033013~ (2020)[DOI]
- A. A. Maksutov, "Methods of Deepfake Detection Based on Machine Learning", 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp.408~411 (2020)
- Singh, A., "DeepFake Video Detection: A Time-Distributed Approach", SN COMPUT. SCI, 1(212), pp.212~ (2020)[DOI]
- "Detecting deepfake videos in the blink of an eye" (2018)[URL]
- Sakthi Kumar Arul Prakash, "Bounded kalman filter method for motion-robust, non-contact heart rate estimation", Biomedical optics express (2018)
- "Neural Ordinary Differential Equations for Intervention Modeling -"[URL]
- "Deepfake Video Detection throughOptical Flow based CNN"[URL]
- Umur Aybars Ciftci, "Fakecatcher: Detection of synthetic portrait videos using biological signals", CoRR, abs, 2019 (1901)
- Dolhansky, B., "The deepfake detection challenge dataset", arXiv preprint arXiv:2006.07397, ~ (2020)
- Gandhi, A., "Adversarial perturbations fool deepfake detectors", arXiv preprint arXiv:2003.10596, ~ (2020)
- Hasan, H. R., "Combating deepfake videos using blockchain and smart contracts", ~ (2019)
- Neekhara, P., "Adversarial deepfakes : evaluating vulnerability of deepfake detectors to adversarial examples", arXiv preprint arXiv:2002.12749, ~ (2020)
- P.-L. Hsieh, "Unconstrained realtime facial performance capture", Computer Vision and Pattern Recognition (CVPR) (2015)
- Marissa Koopman, "Detection of deepfake video manipulation", ~ (2018)
- Punnappurath, A., "Learning raw image reconstruction-aware deep image compressors", IEEE Transactions on Pattern Analysis and Machine Intelligence (2019)[DOI]
- Cheng, Z., "Energy compaction-based image compression using convolutional autoencoder", IEEE Transactions on Multimedia (2019)[DOI]
- Chorowski, J., "Unsupervised speech representation learning using wavenet autoencoders", IEEE, 2019, pp.27(12), 2041~2053
- "Faceswap: Deepfakes software for all"[URL]
- Schroff, F., "Facenet: A unified embedding for face recognition and clustering", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.815~823 (2015)
- "Exposing DeepFake Videos By Detecting Face Warping Artifacts -Yuezun Li, Siwei Lyu -"[URL]
- Korshunov, P, "Speaker inconsistency detection in tampered video", 2018 26th European Signal Processing Conference (EUSIPCO), pp.2375~2379 (2018), IEEE
- Galbally, J, "Face anti-spoofing based on general image quality assessment", 2014 22nd International Conference on Pattern Recognition, pp.1173~1178 (2014), IEEE
- Zhang, Y, "Automated face swapping and its detection", 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP), pp.15~19 (2017), IEEE
- Bai, S., "Growing random forest on deep convolutional neural networks for scene categorization", Expert Systems with Applications, 71, pp.279~287 (2017)
- Kumari, N. M. J, "Prognosis of Diseases Using Machine Learning Algorithms: A Survey", 2018International Conference on Current Trends towards Converging Technologies (ICCTCT), pp.1~9 (2018), IEEE
- 이혜문, "Development of Handwriting Recognition Using Deep Learning in Unity3D", 한국컴퓨터게임학회논문지, 32(1), pp.51~59 (2019), (사)한국컴퓨터게임학회[DOI]