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Related Experiment Video

Updated: May 13, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.2K

Hybrid deep feature integration model for robust deepfake detection using transfer-learned neural networks.

Sirisha Potluri1, Srikar Prabhas Kandagatla2, Sachi Nandan Mohanty3

  • 1Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Bowrampet, Hyderabad, Telangana, India.

Frontiers in Artificial Intelligence
|March 13, 2026
PubMed
Summary

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Masking and Demasking Agents01:19

Masking and Demasking Agents

EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on the metal...

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This study introduces DAAL-NET, a novel hybrid deepfake detection model. It offers efficient, artifact-resistant detection of deepfake content in images and videos, improving digital security.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Digital Forensics

Background:

  • The proliferation of deepfake technology necessitates advanced methods for digital security and media authenticity verification.
  • Current deepfake detection methods, often reliant on deep learning, face challenges with computational cost, resource intensity, and dataset bias, limiting real-world applicability.

Purpose of the Study:

  • To develop a lightweight, artifact-resistant deepfake detection framework capable of analyzing both spatial patterns and temporal inconsistencies.
  • To propose a hybrid approach that overcomes the limitations of existing deepfake detection techniques.

Main Methods:

  • Developed DAAL-NET, a hybrid deepfake detection model featuring a Bi-stream architecture.
  • Incorporated a Local Forensics Encoder with Learnable Frequency Attention for high-frequency manipulation analysis.
Keywords:
DALL-NETbi-stream neural networksdeep fake detectionlearnable frequency attentionmotion irregularity encodertemporal attention gated recurrent unit

Related Experiment Videos

Last Updated: May 13, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.2K
  • Utilized a Motion Irregularity Encoder with depth-wise temporal convolutions and gated recurrent units for motion gap detection.
  • Implemented a Multi-Stream Interaction Module for spatial-temporal fusion via cross-attention.
  • Introduced an Artifact Confidence Calibration Layer for enhanced reliability.

    • Main Results:

    • The DAAL-NET model demonstrated superior performance in enhancing macro-F1 scores and reducing calibration error on benchmark datasets (Celeb-DFv2, Kaggle).
    • Achieved improved temporal robustness compared to existing baseline models.
    • Showcased competitive results under constrained computational resources.

    Conclusions:

    • DAAL-NET offers an effective and efficient solution for deepfake content detection.
    • The model's lightweight and robust nature makes it suitable for real-world forensic applications, media authentication, low-power devices, and scalable screening pipelines.