In-The-Wild deepfake detection using adaptable CNN models with visual class activation mapping for improved accuracy

Muhammad Salihin, Saealal and Mohd Zamri, Ibrahim and Mohd Ibrahim, Shapiai and Norasyikin, Fadilah (2023) In-The-Wild deepfake detection using adaptable CNN models with visual class activation mapping for improved accuracy. In: 2023 5th International Conference on Computer Communication and the Internet, ICCCI 2023 , 23-25 June 2023 , Fujisawa. pp. 9-14.. ISBN 979-835032695-6

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DOI/Official URL: https://doi.org/10.1109/ICCCI59363.2023.10210096

Abstract

Deepfake technology has become increasingly sophisticated in recent years, making detecting fake images and videos challenging. This paper investigates the performance of adaptable convolutional neural network (CNN) models for detecting Deepfakes. In-the-wild OpenForensics dataset was used to evaluate four different CNN models (DenseNet121, ResNet18, SqueezeNet, and VGG11) at different batch sizes and with various performance metrics. Results show that the adapted VGG11 model with a batch size of 32 achieved the highest accuracy of 94.46% in detecting Deepfakes, outperforming the other models, with DenseNet121 as the second-best performer achieving an accuracy of 93.89% with the same batch size. Grad-CAM techniques are utilized to visualize the decision-making process within the models, aiding in understanding the Deepfake classification process. These findings provide valuable insights into the performance of different deep learning models and can guide the selection of an appropriate model for a specific application.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Batch size; Convolution neural network; Deep learning; Deepfake; Grad-CAM visualization
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	Institute of Postgraduate Studies College of Engineering Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	06 Nov 2023 04:24
Last Modified:	06 Nov 2023 04:24
URI:	http://umpir.ump.edu.my/id/eprint/38714
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