PhyTrace: Tracing Physical Inconsistency in AI-Generated Images via ISP Emulation

Area of Science:

• Computer Vision
• Cybersecurity
• Digital Forensics

Background:

• AI-generated images pose a significant cybersecurity threat due to their high realism.
• Existing detection methods often rely on fixed models, limiting their adaptability to new data and model updates.

Purpose of the Study:

• To develop a novel, adaptable, and robust method for detecting AI-generated images.
• To overcome the limitations of existing detection models by focusing on the fundamental imaging process.

Main Methods:

• The study focuses on the Image Signal Processing (ISP) pipeline, a process inherent to real images but absent in AI-generated ones.
• A novel training-free method, PhyTrace, was developed to analyze and amplify physical variations within ISP modules.
• PhyTrace enforces physical consistency constraints within the ISP to differentiate real from AI-generated images.

Main Results:

• PhyTrace effectively detects a wide range of AI-generated images by leveraging physical differences introduced during ISP transformations.
• The method demonstrated superior average precision and generalization capabilities across 18 diverse test sets.
• Distinct distribution patterns between real and AI-generated images were identified through PhyTrace's analysis.

Conclusions:

• PhyTrace offers a robust and generalizable solution for AI-generated image detection in open-world scenarios.
• The training-free nature and independence from specific generative models make PhyTrace a versatile tool.
• Focusing on the physical imaging process provides a more resilient approach to detecting sophisticated AI-generated content.