Swapped Face Detection: AI-Based Method and Evaluation for Different Face Swap Algorithms DOI

Mikhail Haleev

Published: Oct. 30, 2024

Language: Английский

OpenAI's Sora and Google's Veo 2 in Action: A Narrative Review of Artificial Intelligence-driven Video Generation Models Transforming Healthcare DOI Open Access

Mohamad-Hani Temsah,

Rakan I. Nazer,

Ibraheem Altamimi

et al.

Cureus, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 17, 2025

The rapid evolution of generative artificial intelligence (AI) has introduced transformative technologies across various domains, with text-to-video (T2V) generation models emerging as innovations in the field. This narrative review explores potential T2V AI used healthcare, focusing on their applications, challenges, and future directions. Advanced platforms, such Sora Turbo (OpenAI, Inc., San Francisco, California, United States) Veo 2 (Google LLC, Mountain View, States), both announced December 2024, offer capability to generate high-fidelity video contents. Such could revolutionize healthcare by providing tailored videos for patient education, enhancing medical training, possibly optimizing telemedicine. We conducted a comprehensive literature search databases including PubMed Google Scholar, identified 41 relevant studies published between 2020 2024. Our findings reveal significant possible benefits improving standardizing customized remote consultations. However, critical challenges persist, risks misinformation (or deepfake), privacy breaches, ethical concerns, limitations authenticity. Detection mechanisms deepfakes regulatory frameworks remain underdeveloped, necessitating further interdisciplinary research vigilant policy development. Future advancements enable real-time visualizations augmented reality training. achieving these will require addressing accessibility ensure equitable implementation prevent disparities. By fostering collaboration among stakeholders, systems technologists, transform global into more effective, universal, innovative system while safeguarding against its misuse.

Language: Английский

Citations

2
Advances in DeepFake detection algorithms: Exploring fusion techniques in single and multi-modal approach DOI
Ashish Kumar,

Divya Singh,

Rachna Jain

et al.

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 102993 - 102993

Published: Feb. 1, 2025

Language: Английский

Citations

2
Robust deepfake detection using multi-scale feature fusion DOI
G. Yogarajan,

S. Soundhariya,

R. Harini

et al.

Multimedia Tools and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: March 28, 2025

Language: Английский

Citations

0
Deepfake detection: Enhancing performance with spatiotemporal texture and deep learning feature fusion DOI Creative Commons

Abdelwahab Almestekawy,

Hala H. Zayed, Ahmed Taha

et al.

Egyptian Informatics Journal, Journal Year: 2024, Volume and Issue: 27, P. 100535 - 100535

Published: Sept. 1, 2024

Language: Английский

Citations

2
MCGAN—a cutting edge approach to real time investigate of multimedia deepfake multi collaboration of deep generative adversarial networks with transfer learning DOI Creative Commons
Shahid Karim, Xinjun Liu, Abdullah Ayub Khan

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 26, 2024

The proliferation of multimedia-based deepfake content in recent years has posed significant challenges to information security and authenticity, necessitating the use methods beyond dependable dynamic detection. In this paper, we utilize powerful combination Deep Generative Adversarial Networks (GANs) Transfer Learning (TL) introduce a new technique for identifying deepfakes multimedia systems. Each GAN architectures may be customized detect subtle changes different formats by combining their advantages. A multi-collaborative framework called "MCGAN" is developed because it contains audio, video, image files. This compared other state-of-the-art techniques estimate overall fluctuation based on performance, improving accuracy rate up 17.333% strengthening detection hierarchy. order accelerate training process enable system respond rapidly novel patterns that indicate deepfakes, TL employs pre-train same databases. When comes contents proposed method performs quite well. range scenarios, enhances real-time capabilities while preserving high level accuracy. progressive hierarchy ensures integrity digital world related research taken into consideration development.

Language: Английский

Citations

2
Swapped Face Detection: AI-Based Method and Evaluation for Different Face Swap Algorithms DOI

Mikhail Haleev

Published: Oct. 30, 2024

Language: Английский

Citations

0