Next Article in Journal
Enhancing Voltage and Power Output Through the Structural Optimization of Coil–Magnet Transducers in Electromagnetic Vibration Energy Harvesters
Previous Article in Journal
Putting the Synthetic Global Navigation Satellite System Meta-Signal Paradigm into Practice: Application to Automotive Market Devices
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Engineering Proceedings
Volume 87
Issue 1
10.3390/engproc2025087036
engproc-logo
Submit to this Journal
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Proceeding Paper

Practical Evaluation and Performance Analysis for Deepfake Detection Using Advanced AI Models †

by
Bikash Ranjan Barik
,
Ankush Nayak
,
Adyasha Biswal
and
Neelamadhab Padhy
*
School of Engineering & Technology, Gandhi Institute of Engineering and Technology University (GIETU), Gunupur 765022, Odisha, India
*
Author to whom correspondence should be addressed.
†
Presented at the 5th International Electronic Conference on Applied Sciences, 4–6 December 2024; https://sciforum.net/event/ASEC2024.
Eng. Proc. 2025, 87(1), 36; https://doi.org/10.3390/engproc2025087036
Published: 1 April 2025
Browse Figures
Versions Notes

Abstract

In the 21st century of digital technology, deepfakes are increasingly becoming a serious issue across the globe. We have many machine learning and deep learning algorithms that are meant to serve humanity, but nowadays, these algorithms are the main cause of deepfake media, which can affect human life. This study aimed to create a model for recognizing deepfake media or manipulated media using deep learning and machine learning algorithms. The dataset we required for training the model was collected from online sources, and we created some GAN-generated images. Then, we created a model by using the MTCNN, InceptionResNetV1, and FaceNet_PyTorch. All the algorithms gave an excellent result, with an accuracy of 95% by the MTCNN, 98% by InceptionResNetV1, and 98% by Facenet_pytorch.
Keywords: deep learning; deepfake recognition; machine learning; MTCNN deep learning; deepfake recognition; machine learning; MTCNN

Share and Cite

MDPI and ACS Style

Barik, B.R.; Nayak, A.; Biswal, A.; Padhy, N. Practical Evaluation and Performance Analysis for Deepfake Detection Using Advanced AI Models. Eng. Proc. 2025, 87, 36. https://doi.org/10.3390/engproc2025087036

AMA Style

Barik BR, Nayak A, Biswal A, Padhy N. Practical Evaluation and Performance Analysis for Deepfake Detection Using Advanced AI Models. Engineering Proceedings. 2025; 87(1):36. https://doi.org/10.3390/engproc2025087036

Chicago/Turabian Style

Barik, Bikash Ranjan, Ankush Nayak, Adyasha Biswal, and Neelamadhab Padhy. 2025. "Practical Evaluation and Performance Analysis for Deepfake Detection Using Advanced AI Models" Engineering Proceedings 87, no. 1: 36. https://doi.org/10.3390/engproc2025087036

APA Style

Barik, B. R., Nayak, A., Biswal, A., & Padhy, N. (2025). Practical Evaluation and Performance Analysis for Deepfake Detection Using Advanced AI Models. Engineering Proceedings, 87(1), 36. https://doi.org/10.3390/engproc2025087036

Article Metrics

Back to TopTop