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

Pavan C Shekar, Pawan Soni, Vivek Kanhangad

TL;DR

HyperFake addresses the instability of RGB-based deepfake detectors by reconstructing 31-channel hyperspectral data from standard RGB videos, enabling access to spectral traces invisible to conventional methods. The approach combines an enhanced MST++ model for hyperspectral estimation, a spectral attention mechanism to select discriminative bands, and an EfficientNet-B0 classifier tailored for spectral features, trained on ARAD 1K and evaluated on FaceForensics++. Results show strong accuracy and generalization across deepfake styles, highlighting the practical potential of hardware-light hyperspectral analysis for digital media security.

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.

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

TL;DR

HyperFake addresses the instability of RGB-based deepfake detectors by reconstructing 31-channel hyperspectral data from standard RGB videos, enabling access to spectral traces invisible to conventional methods. The approach combines an enhanced MST++ model for hyperspectral estimation, a spectral attention mechanism to select discriminative bands, and an EfficientNet-B0 classifier tailored for spectral features, trained on ARAD 1K and evaluated on FaceForensics++. Results show strong accuracy and generalization across deepfake styles, highlighting the practical potential of hardware-light hyperspectral analysis for digital media security.

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.

Paper Structure

This paper contains 25 sections, 4 equations, 3 figures, 1 table.

Table of Contents

  1. Motivation
  2. The Power of Hyperspectral Imaging
  3. Our Solution: HyperFake
  4. Contributions
  5. Related Work
  6. Evolution of Deepfake Detection
  7. Multi-Modal and Self-Supervised Approaches
  8. Spectral Analysis in Media Forensics
  9. Methodology
  10. Overview
  11. Hyperspectral Image Reconstruction
  12. MST++ for Hyperspectral Estimation
  13. Training for Hyperspectral Reconstruction
  14. Spectral Attention Mechanism
  15. Attention-Based Feature Selection
  16. ...and 10 more sections

Figures (3)

  • Figure 1: HyperFake pipeline architecture for Deepfake detection. RGB input is processed by MST++ to generate a 31-channel hyperspectral image. The spectral attention module selects key features, reducing them to three channels. EfficientNetB0 then classifies the input as real or fake.
  • Figure 2: Spectral Attention Module: Transforming 31-Channel Hyperspectral Data to 3-Channel Representation Diagram illustrating the spectral attention mechanism that identifies and emphasizes the most critical spectral features, reducing dimensionality while preserving key discriminative information for deepfake detection.
  • Figure 3: Comparison of real and fake images with hyperspectral reconstructions.The first column shows real images, the second shows fake images, and the last three display selected spectral channels (channels 8, 16, 31) from the reconstructed fakes. These reconstructions highlight subtle differences, helping detect deepfakes.