HyperFake: Hyperspectral Reconstruction and Attention-Guided Analysis for Advanced Deepfake Detection
This addresses the threat of deepfakes to digital media security by improving detection robustness, though it is incremental as it builds on existing hyperspectral reconstruction and classification methods.
The paper tackles the problem of deepfake detection by reconstructing hyperspectral data from RGB videos to reveal hidden manipulation traces, achieving more accurate and generalizable detection across different deepfake styles and datasets without needing expensive hyperspectral cameras.
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.