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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 3

Efficient and self-explainable graph neural networks for deepfake detection

Lyu Renkun1
Sun Peng1,2
Lang Yubo1
Shen Zhe3
Meng Hui1
Zhou Chunbing1
1. Dept. of Public Security Information Technology and Intelligence, Criminal Investigation Police University of China, Shenyang 110854, China
2. Key Lab of Forensic, Ministry of Justice, Shanghai 200063, China
3. Civil Aviation College, Shenyang Aerospace University, Shenyang 110135, China
DOI: 10.19734/j.issn.1001-3695.2025.07.0209

Abstract

To address the limitations of existing deep learning-based methods in deepfake detection—namely, suboptimal performance and low interpretability, this paper proposes a method based on an Efficient and Self-Explainable Graph Neural Network (SE-EffGNN) . The approach consists of three components: graph structure construction, model prediction, and explanatory analysis. First, prior knowledge and local spatial information were integrated to model input videos as semantically guided graph structures, enhancing the sensitivity to forgery traces. Next, the SE-EffGNN was designed to encode node features across multiple dimensions, update node representations via combined distance-weighted aggregation and adjacency-edge aggregation, and incorporate channel gating and node attention mechanisms to capture discriminative features for classification. Finally, parameters from the distance weighting, feature encoding, and node attention modules were visualized to provide self-explanatory capability. Experimental results show that the proposed method achieved an average AUC of 0.9946 on mainstream datasets. The visualization of learned parameters offers clear interpretative support, confirming that SE-EffGNN maintains high detection performance while demonstrating excellent explainability.

Foundation Support

辽宁省教育厅科技创新团队项目(LJ222410175007)、司法部司法鉴定重点实验室开放课题(KF202317)、辽宁省研究生教育教学改革研究资助项目(LNYJG2023317)、沈阳市科技计划项目社会治理科技专项(24-213-3-41)、校级重点攻关计划项目(ZDGGJH2507)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.07.0209
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 3

Publish History

[2025-09-25] Accepted Paper

Cite This Article

吕仁堃, 孙鹏, 郎宇博, 等. 面向深度伪造检测的高效自解释图神经网络 [J]. 计算机应用研究, 2026, 43 (3). (2025-11-18). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0209. (Lyu Renkun, Sun Peng, Lang Yubo, et al. Efficient and self-explainable graph neural networks for deepfake detection [J]. Application Research of Computers, 2026, 43 (3). (2025-11-18). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0209. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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