Semantic-Aware Lightweight AI Model for Deepfake Image Detection in Online Retail Platforms

Vincent Shin-Hung Pan, Akshat Gaurav, Saoucene Mahfoudh, Turki Althaqafi, Wadee Alhalabi, Ramakrishnan Raman and Ching-Hsien Hsu
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Vincent Shin-Hung Pan: Department of Information Management, Chaoyang University of Technology, Taiwan
Akshat Gaurav: Ronin Institute, USA & Center for Interdisciplinary Research, University of Petroleum and Energy Studies, Dehradun, India
Saoucene Mahfoudh: School of Engineering, Computing, and Design, Dar Al-Hekma University, Jeddah, Saudi Arabia
Turki Althaqafi: Computer Science Department, School of Engineering, Computing and Design, Dar Al-Hekma University, Jeddah, Saudi Arabia
Wadee Alhalabi: Immersive Virtual Reality Research Group, Department of Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia & Computer Science Department, School of Engineering, Computing, and Design, Dar Al-Hekma University, Jeddah, Saudi Arabia
Ramakrishnan Raman: Symbiosis International University, Pune, India
Ching-Hsien Hsu: Department of Computer Science and Information Engineering, Asia University, Taiwan

International Journal on Semantic Web and Information Systems (IJSWIS), 2025, vol. 21, issue 1, 1-16

Abstract: Deepfake detection in e-commerce platforms demands lightweight, efficient, and accurate models capable of real-time performance. This paper proposes a semantic-aware, lightweight ShuffleNet-based model optimized for detecting image-based deepfake content. The proposed model integrates ShuffleNet with a Semantic Knowledge Graph (SKG) for enhanced deepfake image detection. The SKG links extracted visual features with contextual metadata, enabling a more interpretable and knowledge-driven classification process. The proposed model achieves an accuracy of 76.15%, precision of 80.97%, recall of 76.15%, and F1-score of 74.79% while significantly reducing computational costs. Compared to standard architectures like DenseNet, MobileNet, and EfficientNet, the model achieves the lowest FLOPs (295.57M) and parameter count (1.26M). These results highlight the model's ability to outperform existing architectures in balancing performance and computational efficiency. The proposed solution is ideal for real-time, resource-constrained environments, positioning it as an effective tool for combating deepfake challenges in online retail.

Date: 2025
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