Fractal Characterization of Low-Correlation Signals in AI-Generated Image Detection

TL;DR

This paper introduces a fractal-based method to detect AI-generated images by analyzing low-correlation signals, demonstrating improved robustness over existing techniques.

Contribution

It proposes a novel fractal analysis approach to quantify low-correlation signals for more effective AI-generated image detection.

Findings

The method effectively captures statistical anomalies in synthetic images.

Experimental results show superior detection performance and robustness.

The approach is applicable beyond face images to all AI-generated images.

Abstract

AI-generated imagery has reached near-photorealistic fidelity, yet this technology poses significant threats to information security and societal trust. Existing deepfake detection methods often exhibit limited robustness in open-world scenarios. To address this limitation, this paper investigates intrinsic discrepancies between synthetic and authentic images from a signal-level perspective. Our analysis reveals that low-correlation signals serve as distinctive markers for differentiating AI-generated imagery from real photographs. Building on this insight, we introduce a novel method for quantifying these signals based on fractal theory. By analyzing the fractal characteristics of low-correlation signals, our method effectively captures the subtle statistical anomalies inherent to the synthesis process. Extensive experimental results demonstrate the method's robustness and superior detection performance. This work emphasizes the need to shift research focus to a new signal-level direction for deepfake detection. Theoretically, this proposed approach is not limited to face image identification but can be applied to all AI-generated image detection tasks. This study provides a new research direction for deepfake detection.