🤖 AI Summary
This work addresses the limited robustness of existing deepfake detection methods in open-world scenarios, where distinguishing highly realistic AI-generated images from authentic ones remains challenging. The authors propose a novel signal-level approach that, for the first time, integrates fractal theory into low-correlation signal analysis to uncover subtle statistical anomalies introduced during image synthesis by generative models. By operating directly on intrinsic signal characteristics rather than relying on high-level semantic features, the method overcomes key limitations of conventional detectors. The resulting general-purpose, signal-based detection model demonstrates superior robustness and cross-dataset generalization, significantly improving detection accuracy across diverse AI-generated imagery in unconstrained real-world settings.
📝 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.