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Related Experiment Videos

A spatiotemporal defect-integrated deepfake video detection and forgery algorithm attribution model.

Chang Liu1, Xiaoxuan Wang2, Yinfu Zhang2

  • 1School of Police Law Enforcement Abilities Training, People's Public Security University of China, Beijing, 100038, China. 20216879@ppsuc.edu.cn.

Scientific Reports
|June 8, 2026
PubMed
Summary
This summary is machine-generated.

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Understanding Deception01:14

Understanding Deception

Deception is a pervasive aspect of human communication. Empirical studies have shown that most individuals engage in some form of deceit on a daily basis, with approximately 20% of social exchanges involving deceptive elements. Lying follows a developmental trajectory, peaking during adolescence and declining with age, possibly due to the maturation of cognitive control and social accountability.Cognitive and Social Factors in Deception DetectionDespite its prevalence, accurately detecting...

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This study introduces a novel framework for detecting deepfake videos and attributing their generation algorithms. The method effectively identifies AI-generated content and its source, enhancing digital security against misinformation.

Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Digital Forensics

Background:

  • Deepfake technology presents significant safety concerns due to its use in sophisticated fraudulent activities.
  • Existing deepfake detection methods often overlook subtle manipulation traces unique to specific forgery algorithms.
  • Attributing deepfakes to their generation algorithms is crucial for understanding forgery types and mitigating misinformation.

Purpose of the Study:

  • To propose a spatiotemporal artifact-aware framework for simultaneous deepfake video detection and forgery algorithm attribution.
  • To address the limitations of current research by capturing unique manipulation traces and enabling source attribution.

Main Methods:

  • Combines Convolutional Neural Networks (CNNs) for local feature learning and Transformers for long-range dependency modeling.

Related Experiment Videos

  • Integrates frequency-domain filtering to enhance the capture of subtle synthesis traces within convolutional features.
  • Utilizes multi-layer outputs (middle and deep) to capture multi-scale forgery traces and fuses predictions for final results.
  • Employs a multi-loss optimization strategy (cross-entropy, triplet, hard sample mining) for discriminative feature learning.

    • Main Results:

    • Achieved 97.86±0.18% detection accuracy and 99.81±0.11% AUC on the FaceForensics++ dataset.
    • Attained 98.42±0.15% accuracy for forgery algorithm attribution.
    • Outperformed most state-of-the-art approaches in both detection and attribution tasks.

    Conclusions:

    • The proposed framework effectively detects deepfake videos and attributes their generation algorithms.
    • The spatiotemporal artifact-aware approach enhances the model's ability to learn robust and discriminative forgery features.
    • This work contributes to improved digital security and the fight against AI-generated misinformation.