This paper provides a systematic review of mainstream deepfake generation models and methods, including Autoencoders, Variational Autoencoders, Variational Autoencoders (VAE), Convolutional Neural Networks (CNN), and Convolutional Neural Networks (CNN), and Generative Adversarial Networks (GAN) etc.
This work proposes a new GAN-based face hallucination method primarily based on the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN), and presents a personalised adaptation of ESRGAN that employs the VGG16 architecture with a compact pre-trained version.
Sheetal S. Patil, A. Pawar, Nilofar Mulla et al.· International Journal of Eng...· 0 citations
This paper summarizes the mainstream optimization research directions, such as lightweight, multi architecture integration, and multi condition controllable generation, such as lightweight, multi architecture integration, and multi condition controllable generation.
Zhi-Li Zheng· Applied and Computational En...· 0 citations
The evolution of sophisticated generative artificial intelligence has led to the rapid development of very realistic manipulated images and videos, posing substantial risks for digital trust, cyber security, and multimedia authenticity. Advanced Deepfake generation technologies result in the creation of believable forg...
Bella Inba Suganthi V, S. Jose· International Journal of Sci...· 0 citations
This research systematically analyzes the generative mechanisms and detection techniques ofDeepfake, evaluating the effectiveness and limitations of mainstream approaches such as frequency domain analysis and physiological signal recognition and proposes forward-looking solutions to address future challenges.
This research proposes a robust deep fake detection pipeline, leveraging a custom dataset created using Roboflow, and trained and evaluated four YOLO-based models YOLOv8, YOLOv8, YOLOv9, YOLOv10, and YOLOv11 for the detection task.
A. Al Noman, Abdullah Al Afiq, Md. Humayun Kabir et al.· Discover Computing· 0 citations
A multidimensional, standardized comparison of three generative models: Generative Adversarial Networks, Variational Autoencoders and Diffusion Models is conducted, based on extensive literature review, combining theoretical analysis and experiments to provide a scientific basis for model selection and support decision...
Qirui Guo, Yu-Xing Hu, Zhe-Jia Wang· Proceedings of the 3rd Inter...· 0 citations
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