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Single-layer KAN for deepfake classification: Balancing efficiency and performance in resource constrained

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This study introduces a lightweight Kolmogorov-Arnold Network (KAN) for efficient deepfake detection on edge devices. KAN achieves high accuracy with significantly reduced computational resources compared to traditional methods.

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Cybersecurity

Background:

  • Deepfakes, AI-generated synthetic media, pose a significant threat to digital content authenticity.
  • Conventional deepfake detection methods, like Convolutional Neural Networks (CNNs), are computationally intensive, limiting their use on resource-constrained devices.
  • The need for efficient, real-time deepfake detection on edge devices is critical.

Purpose of the Study:

  • To evaluate the efficacy of a single-layer Kolmogorov-Arnold Network (KAN) for deepfake classification.
  • To assess KAN's performance in terms of accuracy, memory footprint, parameter count, and FLOPs.
  • To determine KAN's suitability for deployment on edge devices for real-time deepfake detection.

Main Methods:

  • A single-layer Kolmogorov-Arnold Network (KAN) with 200 nodes was implemented for deepfake classification.
  • The KAN model was evaluated on benchmark datasets: FaceForensics++ and Celeb-DF.
  • Performance metrics including accuracy, memory usage, parameter count, and Floating Point Operations (FLOPs) were measured and compared to state-of-the-art CNNs.

Main Results:

  • KAN achieved 95.01% accuracy on the FaceForensics++ dataset and 88.32% on the Celeb-DF dataset.
  • The KAN model demonstrated significant efficiency, requiring only 52.4 MB of memory, 13.11 million parameters, and 26.21 million FLOPs.
  • These results indicate a substantial reduction in computational resources compared to existing CNN-based methods.

Conclusions:

  • The Kolmogorov-Arnold Network (KAN) presents a viable and efficient solution for deepfake detection on edge devices.
  • KAN's low resource requirements make it suitable for real-time applications on smartphones and IoT systems.
  • Future research should explore KAN's robustness against adversarial attacks and its broader applicability in digital media forensics.