Improving DeepFake Detection: A Comprehensive Review of Adversarial Robustness, Real-Time Processing and Evaluation Metrics

Clicks: 1
ID: 312887
2024
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #486 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
This review analyzes 30 studies on deepfake detection. It focuses on three areas: adversarial robustness, real-time processing, and evaluation metrics. Deepfake technology is making rapid progress. It poses serious threats to digital security. We need strong, efficient detection models. The review exposes three key factors that boost detection system power. They are: adversarial training, GAN-based methods, and lightweight designs. They boost both resilience and efficiency. But challenges remain. We need real-time processing and standardized tests. They must capture the nuances of deepfake detection. The findings show a need for more research. It must address new threats, improve detection models, and set real-world benchmarks. The study stresses the need to improve deepfake detectors. We must integrate advanced training, optimize methods, and refine metrics. They must be robust, accurate, and adaptable to new digital threats.
Reference Key
imported_1777057617_69ebbf5145995 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Najaf Saeed, Gohar Mumtaz, Muqaddas Yaqub, Muhammad Haroon Ahmad
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/572
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.