Boylan, JF Will deepfake technology destroy democracy? (New York Times, 2018).
Google Scholar
Harwell, D. Scarlett Johansson, on AI-generated fake sex videos: ‘I can’t stop someone from cutting and pasting images of me’. J. Washington Post 3112 (2018).
Google Scholar
Massoud, M. and others. Deepfake Generation and Detection: State-of-the-Art Open Challenges, Countermeasures, and Progress. Application intelligence. 531–53 (2022).
Google Scholar
Amin, R., Al Ghamdi, MA, Almotiri, SH & Alruily, M. Deep Learning Healthcare Technologies: Problems, Challenges and Opportunities. IEEE access 998523–98541 (2021).
Turek, MJ Defense Advanced Research Projects Agencyhttps://www.darpa.mil/program/media-forensics. Media Forensics (MediFor). roll. 10 (2019).
Schroepfer, MJF dataset creation and deepfake challenge. Artif. intelligence. Five263 (2019).
Google Scholar
Kibriya, H. and others. A novel and effective brain tumor classification model using Deep Feature Fusion and a well-known machine learning classifier. roll. 2022 (2022).
Rafique, R., Nawaz, M., Kibriya, H. & Masood, M. DeepFake detection using error level analysis and deep learning.of 2021 4th International Conference on Computing and Information Science (ICCIS)1–4 (IEEE, 2021).
Güera, D. & Delp, EJ Deepfake video detection using recurrent neural networks.of 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS)1–6 (IEEE, 2018).
Aleem, S. and others. Machine Learning Algorithms for Depression: Diagnosis, Insights, and Research Directions. electronics 11(7), 1111 (2022).
Pavan Kumar, M. & Jayagopal, P. Generative Adversarial Networks: A Survey of Applications and Challenges. Inside and outside J. Multimed. information. Ten(1), 1–24 (2021).
Mansur, M. and others. A machine learning approach for non-invasive fall detection using Kinect. Multimed.tool app 81(11), 15491–15519 (2022).
Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C. & Nießner, M. Face2face: Real-time face capture and reconstruction from RGB video.of Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition2387–2395 (2016).
Shad, HS and others. A comparative analysis of deepfake image detection methods using convolutional neural networks. roll. 2021 (Heisei 33).
Matern, F., Riess, C. & Stamminger, M. Expose deepfakes and facial manipulations using visual artifacts.of 2019 IEEE Winter Application of Computer Vision Workshops (WACVW)83–92 (IEEE, 2019).
Agarwal, S., Farid, H., Gu, Y., He, M., Nagano, K. & Li, H. Protecting world leaders from deep fraud. CVPR Workshop. roll. 1. 38 (2019).
Ciftci, UA, Demir, I. & Yin, L. Fakecatcher: Detecting Synthetic Portrait Videos Using Biometric Signals (Google Patent, 2021).
Google Scholar
Yang, X., Li, Y. & Lyu, S. Exposing deep fakes using inconsistent head poses.of ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 8261–8265. (IEEE, 2019).
Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J. & Nießner, M. Faceforensics++: Learning to Detect Manipulated Face Images.of Proceedings of the IEEE/CVF International Conference on Computer Vision1–11 (2019).
McCloskey, S. & Albright, M. Detection of GAN-generated images using saturated cues.of 2019 IEEE International Conference on Image Processing (ICIP)4584–4588. (IEEE, 2019).
Abd Elaziz, M., Dahou, A., Orabi, DA, Alshathri, S., Soliman, EM & Ewees, AAJM Hybrid multitasking learning framework with Fire Hawk optimizer for Arabic fake news detection. roll. 11(2). 258 (2023).
Wodajo, D. & Atnafu, SJAPA Deepfake Video Detection Using Convolutional Vision Transformers (2021).
Guarnera, L., Giudice, O. & Battiato, S. Deepfake detection by analysis of convolutional traces.of IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshop Minutes666–667 (2020).
Nguyen, HH, Fang, F., Yamagishi, J. & Echizen, I. Multi-task learning for detecting and segmenting manipulated facial images and videos.of 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS)1–8. (IEEE, 2019).
Khalil, SS, Youssef, SM & Saleh, SNJFI iCaps-Dfake: An Integrated Capsule-Based Model for Deepfake Image and Video Detection. roll. 13(4). 93 (2021).
Afchar, D., Nozick, V., Yamagishi, J. & Echizen, I. Mesonet: A Compact Face Video Forgery Detection Network.of 2018 IEEE International Workshop on Information Forensics and Security (WIFS)1–7 (IEEE, 2018).
Wang, Y. & Dantcheva, A. A video is worth over 1000 lies. Comparison of 3DCNN approaches for detecting deepfakes.of 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)515–519. (IEEE, 2020).
Cozzolino, D., Thies, J., Rössler, A., Riess, C., Nießner, M. & Verdoliva, LJAPA Forensictransfer: Weakly Supervised Domain Adaptation for Forgery Detection (2018).
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, KQ Densely Connected Convolutional Networks.of Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition4700–4708 (2017).
LeCun, Y., Bengio, Y. & Hinton, G. Deep Learning. Nature 521(7553), 436–444 (2015).
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition.of Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition770–778 (2016).
Nida, N., Irtaza, A. & Ilyas, N. Forged face detection using ELA and deep learning techniques.of 2021 International Bourban Conference on Applied Science and Technology (IBCAST)271–275 (IEEE, 2021).
Kibriya, H., Masood, M., Nawaz, M., Rafique, R. & Rehman, S. Multiclass Brain Tumor Classification Using Convolutional Neural Networks and Support Vector Machines.of 2021 Mohammad Ali Jinnah University International Computing Conference (MAJICC)1–4 (IEEE, 2021).
Kibriya, H., Masood, M., Nawaz, M. & Nazir, TJMT Multi-class classification of brain tumors using a novel CNN architecture. Multimed.Tool application 811–17 (2022).
Salman, FM & Abu-Naser, SS Classification of real and fake human faces using deep learning. ijar 6(3), 1–14 (2022).
Google Scholar
Anaraki, AK, Ayati, M. & Kazemi, FJ Brain tumor grading based on magnetic resonance imaging and grading with convolutional neural networks and genetic algorithms. information 39(1), 63–74 (2019).
Google Scholar
Albawi, S., Mohammed, TA & Al-Zawi, S. Understanding Convolutional Neural Networks.of 2017 International Conference on Engineering and Technology (ICET)1–6 (IEEE, 2017).
O’Shea, K. & Nash, RJ Overview of Convolutional Neural Networks (2015).
Szegedy, C. and others. It gets even deeper with convolutions.of Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition1–9 (2015).
Iandola, FN, Han, S., Moskewicz, MW, Ashraf, K., Dally, WJ & Keutzer, KJ SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size (2016).
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition.of Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA. 770–778 (2016).
Introduction to Residual Networkshttps://www.geeksforgeeks.org/introduction-to-residual-networks/ (2020).
Ali, L. and others. Performance evaluation of deep CNN-based crack detection and localization techniques in concrete structures. sensor twenty one(5), 1688 (2021).
Ramzan, F. and others. A deep learning approach for automated diagnosis and multiclass classification of Alzheimer’s disease stages using resting-state fMRI and residual neural networks. J. Med.system 44(2), 1–16 (2020).
Mancini, M., Costante, G., Valigi, P. & Ciarfuglia, TA Fast and Robust Monocular Depth Estimation for Obstacle Detection Using Fully Convolutional Networks.of 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)4296–4303 (IEEE, 2016).
Kasim, N., Rahman, N., Ibrahim, Z. & Mangshor, NA Celebrity Face Recognition Using Deep Learning. J. Electric from Indonesia. Calculate English. Science. 12(2), 476–481 (2018).
Rezgui, D. & Lachiri, Z. ECG biometrics using an SVM-based approach. IEEJ transformer. electricity. electronic.English 11S94–S100 (2016).
YU Computational Intelligence and Photography Lab. Real and fake face detection (2019).
Tolosana, R., Romero-Tapiador, S., Fierrez, J. & Vera-Rodriguez, R. Evolution of Deepfakes: Facial Region Analysis and Fake Detection Performance.of International Conference on Pattern Recognition442–456 (Springer, 2016).
Mehra, A. Detecting Deepfakes Using Capsule Networks with Long Short-Term Memory Networks (University of Twente, 2020).
Google Scholar
Mittal, H., Saraswat, M., Bansal, JC & Nagar, A. Image classification of fake faces using an improved quantum-inspired evolutionary-based feature selection method.of 2020 IEEE Symposium Series on Computational Intelligence (SSCI)989–995 (IEEE, 2020).
Chandani, K. & Arora, M. Automatic face forgery detection using deep neural networks.of Interdisciplinary progress205–214 (Springer, 2021).
Lee, S., Tariq, S., Shin, Y. & Woo, SS Handcrafted Facial Image Manipulation and GAN-Generated Facial Image Detection Using Shallow-FakeFaceNet. Application software Compute. 105107256 (2021).
