Citation

Pandey, Pawan and Solanki, Arun and Sharma, Sanjay Kumar and Jhanjhi, N.Z. and Mehmood, Raja Majid (2026) Rethinking deepfake detection evaluation: A principled multi-dimensional relative assessment. Machine Learning with Applications, 26. p. 101011. ISSN 2666-8270

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Abstract

Deepfake videos have created serious concerns about the reliability and authenticity of digital media. There exist several sophisticated algorithms for detecting deepfakes, while their evaluations are often limited to individual metrics. In order to address the limitation, this study proposes a new comprehensive relative assessment framework for evaluating deepfake detection models across multiple aspects: resilience to standard image manipulations, effectiveness, and cross-dataset generalizability. The weights of different performance measures are dynamically determined based on their relative discriminative contributions using an entropy-based weighting method, along with a stability-aware penalty mechanism to discourage inconsistent performance. Experimental evaluation is performed using advanced deepfake detection architectures on benchmark datasets, including DFDC, Celeb-DF (v2), and FaceForensics++. The proposed framework facilitates a more comprehensive evaluation through a unified and interpretive score for comparative assessment of deepfake detection models across multiple performance aspects compared to traditional single-metric approaches.

Item Type:	Article
Uncontrolled Keywords:	Deepfake detection, Multi-dimensional assessment, Performance evaluation, Robustness evaluation, Digital forensics
Subjects:	Q Science > QA Mathematics > QA801-939 Analytic mechanics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	02 Oct 2026 00:42
Last Modified:	02 Oct 2026 00:42
URII:	http://shdl.mmu.edu.my/id/eprint/16809

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