HyperFake: Hyperspectral Reconstruction and Attention-Guided Analysis for Advanced Deepfake Detection

TL;DR

HyperFake introduces a hyperspectral reconstruction approach using spectral attention and efficient classification to improve deepfake detection accuracy and generalization without requiring hyperspectral cameras.

Contribution

It pioneers the use of hyperspectral imaging reconstruction and spectral attention mechanisms for deepfake detection, enhancing robustness and accuracy.

Findings

Improved detection accuracy across multiple datasets.

Enhanced generalization to various deepfake manipulation techniques.

First to leverage hyperspectral reconstruction for deepfake detection.

Abstract

Deepfakes pose a significant threat to digital media security, with current detection methods struggling to generalize across different manipulation techniques and datasets. While recent approaches combine CNN-based architectures with Vision Transformers or leverage multi-modal learning, they remain limited by the inherent constraints of RGB data. We introduce HyperFake, a novel deepfake detection pipeline that reconstructs 31-channel hyperspectral data from standard RGB videos, revealing hidden manipulation traces invisible to conventional methods. Using an improved MST++ architecture, HyperFake enhances hyperspectral reconstruction, while a spectral attention mechanism selects the most critical spectral features for deepfake detection. The refined spectral data is then processed by an EfficientNet-based classifier optimized for spectral analysis, enabling more accurate and generalizable detection across different deepfake styles and datasets, all without the need for expensive hyperspectral cameras. To the best of our knowledge, this is the first approach to leverage hyperspectral imaging reconstruction for deepfake detection, opening new possibilities for detecting increasingly sophisticated manipulations.