Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
August 4, 2025Advances in Research

Deepfake Detection Using Deep Learning: A Review

View Full Paper
Ask AI
Bookmark
Share

Authors

BPBasanta K. PanigrahiSMSiba Prasad MishraCSChinmay Kumar Samal

Discussion

Loading...

Member takes

Overview

This review explores deep learning models for improved deepfake detection, indicating the rising need for reliable mitigation strategies against misinformation.

Key Points

  • The review highlights recent advancements in deepfake detection using deep learning, addressing misinformation concerns.
  • Automated detection methods include generative adversarial networks and convolutional neural networks for identifying fake content.
  • Hybrid frameworks utilize spatial, temporal, and physiological analyses to enhance detection accuracy of deepfakes.
  • Evaluating tools like Sentinel and Fake-catcher showcases the importance of real-time detection in safeguarding digital authenticity.

Cite This Study

Panigrahi et al. (2025) studied this question.

synapsesocial.com/papers/689a0f86e6551bb0af8d09dehttps://doi.org/10.9734/air/2025/v26i41435
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Comprehensive Review of Machine Learning Techniques for DeepFake Detection2025 · 2 citations
  2. 2Unmasking Deepfakes: A Systematic Review of Generation Techniques and Detection Strategies2025 · 1 citations
  3. 3Deep fake Defense Combating Synthetic Media with AI-Powered Detection Tools2024
  4. 4Deepfake Detection: Advances, Challenges, and Future Directions2025 · 2 citations
  5. 5A Machine Learning Approach to Deepfake Detection2025