Light-wDEF: lightweight deepfake evaluation framework for efficient and reliable visual detection

Author First name, Last name, Institution

• Muhammad Yousaf, University of Engineering & Technology Mardan
• Sarwar Shah Khan, Iqra National University
• Babar Shah, Zayed University, Abu Dhabi CampusFollow
• Muhammad Shehzad Khan, University of Engineering & Technology Mardan
• Muhammad Awais Bacha, University of Engineering & Technology Mardan
• Mian Saeed Akbar, University of Engineering & Technology Mardan
• Ijaz Ali, Iqra National University
• Fernando Moreira, Universidade Portucalense Infante D.Henrique

Document Type

Article

Source of Publication

Applied Intelligence

Publication Date

9-1-2026

Abstract

Deepfake technology synthesizes and manipulates digital content to produce artificial videos in which an individual’s identity, facial appearance, or actions are convincingly altered. These forgeries can be highly realistic, posing serious risks to trust in digital media and undermining personal reputation, dignity, and the integrity of public discourse. This paper presents the Lightweight Deepfake Evaluation Framework (Light-wDEF)– a resource-efficient, reproducible framework tailored for large-scale evaluation of visual deepfake detectors on modest hardware. Light-wDEF combines deterministic frame sampling, batched MTCNN face extraction, streaming append-only storage with lazy offset indexing, online Albumentations augmentations, stratified splits with class-aware sampling, and robust training (AdamW, optional focal loss, per-epoch checkpointing with exact resume). Light-wDEF is designed as a reproducible and resource-conscious evaluation framework that focuses on experimental standardization, reproducibility, and efficient benchmarking of deepfake detection models. Using Light-wDEF, ConvNeXt-Base is identified as the most effective visual model in this study on the basis of AUC, balanced accuracy, F1, precision and recall while remaining compatible with Colab T4-class resource constraints. Ten modern visual models were trained and evaluated under identical conditions across five public datasets (Celeb-DF, FaceForensics++, Google DFD, FakeAVCeleb, DFDC Preview). Results show that convolutional families provide the best practical balance of discrimination and efficiency (ConvNeXt-Base achieves AUC≥0.96 and F1≥0.94 on the evaluated sets). The findings also expose limitations of visual-only detection on multimodal corpora such as FakeAVCeleb, motivating future multimodal fusion and domain-adaptation research. Fixed seeds, archived checkpoints, and environment metadata are provided to support exact replication.

DOI Link

10.1007/s10489-026-07460-2

ISSN

0924-669X

Publisher

Springer Science and Business Media LLC

Volume

56

Issue

14

Disciplines

Computer Sciences

Keywords

ConvNeXt, Cross-dataset evaluation, Deep learning, Deepfakes, Light-wDEF, Resource-efficient

Scopus ID

105050681846

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Recommended Citation

Yousaf, Muhammad; Khan, Sarwar Shah; Shah, Babar; Khan, Muhammad Shehzad; Bacha, Muhammad Awais; Akbar, Mian Saeed; Ali, Ijaz; and Moreira, Fernando, "Light-wDEF: lightweight deepfake evaluation framework for efficient and reliable visual detection" (2026). All Works. 8409.
https://zuscholars.zu.ac.ae/works/8409

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Green: A manuscript of this publication is openly available in a repository