Exposing DeepFakes via Hyperspectral Domain Mapping

2025-11-13

AI Summary

Existing DeepFake detection methods predominantly operate in the RGB color space, limiting their capacity to capture subtle forgeries. To address this limitation, we propose HSI-Detect—the first DeepFake detector leveraging hyperspectral domain mapping. Our approach employs a spectral reconstruction network to synthesize 31-channel hyperspectral data from a single RGB image, thereby amplifying generative artifacts across dense spectral dimensions. We further introduce a two-stage deep detection framework that jointly models cross-band anomaly patterns. By transcending the information bottleneck inherent in RGB representations, HSI-Detect achieves state-of-the-art performance on FaceForensics++, outperforming leading RGB-based baselines by +5.2% in AUC. This demonstrates the efficacy and generalizability of hyperspectral representation for authenticating synthetic media.

Technology Category

• Computer Vision: Other Foundations of Computer Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Modern generative and diffusion models produce highly realistic images that can mislead human perception and even sophisticated automated detection systems. Most detection methods operate in RGB space and thus analyze only three spectral channels. We propose HSI-Detect, a two-stage pipeline that reconstructs a 31-channel hyperspectral image from a standard RGB input and performs detection in the hyperspectral domain. Expanding the input representation into denser spectral bands amplifies manipulation artifacts that are often weak or invisible in the RGB domain, particularly in specific frequency bands. We evaluate HSI-Detect across FaceForensics++ dataset and show the consistent improvements over RGB-only baselines, illustrating the promise of spectral-domain mapping for Deepfake detection.

Problem

• Detecting highly realistic fake images that fool human and automated systems
• Overcoming limitations of RGB-only detection with sparse spectral channels
• Amplifying subtle manipulation artifacts through hyperspectral domain mapping

Innovation

• Reconstructs hyperspectral images from RGB inputs
• Detects manipulations in expanded spectral domain
• Amplifies artifacts invisible in standard RGB space