Citation

Nabi, Md Serajun and Yuden, Dema and Fauzi, Mohammad Faizal Ahmad (2025) Deepfake Detection Using ResNet50V2 with Machine Unlearning Integration. In: TENCON 2025 - 2025 IEEE Region 10 Conference (TENCON), 27-30 October 2025, Kota Kinabalu, Malaysia.

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Abstract

The advent of deepfake content has been an enormous challenge in digital media security that necessitates an effective detection system. This study proposes a deepfake detection model based on ResNet50V2 with an associated machine unlearning (MUL) approach to enable selective forgetting of generated data. The model is trained and evaluated on the FaceForensics- 1600 dataset, which consists of real and deepfake video frames, and then undergoes a retraining phase, excluding the forgetting set. Performance before and after unlearning is quantified in terms of classification reports, confusion matrices, and ROC curves. Experimental results show that the model maintains an accuracy of 96% and ROC-AUC of 97% even after unlearning. These findings suggest that MUL can complement model adaptability in dynamic data environments. It also supports compliance with data privacy laws requiring data deletion. The results demonstrate that practical unlearning can be applied to deepfake detection systems without impacting performance, offering a promising solution for ethically adaptive and privacypreserving AI.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Deepfake detection, ResNet50V2, machine un learning, ethical AI, privacy preservation, selective forgetting
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28-70 Management. Industrial Management > HD30.2 Electronic data processing. Information technology. Including artificial intelligence and knowledge management
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	20 Apr 2026 04:40
Last Modified:	20 Apr 2026 04:40
URII:	http://shdl.mmu.edu.my/id/eprint/15790

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