NS-Net: Decoupling CLIP Semantic Information through NULL-Space for Generalizable AI-Generated Image Detection

2025-08-02

AI Summary

Existing AI-generated image detectors exhibit poor generalization to unseen generative models, especially when forged and authentic images share highly similar semantic content. Method: We identify that CLIP’s high-level semantic features impede discrimination of generation artifacts; thus, we propose the NULL-space projection framework to explicitly decouple semantic information from generative traces. We further design a local-aware patch selection mechanism to mitigate semantic bias introduced by global structural cues and integrate contrastive learning to sharpen decision boundaries. Contribution/Results: Our end-to-end detector achieves a 7.4% absolute accuracy gain over state-of-the-art methods on an open-world benchmark covering 40 diverse generative models, demonstrating significantly improved generalization robustness to previously unseen generators.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The rapid progress of generative models, such as GANs and diffusion models, has facilitated the creation of highly realistic images, raising growing concerns over their misuse in security-sensitive domains. While existing detectors perform well under known generative settings, they often fail to generalize to unknown generative models, especially when semantic content between real and fake images is closely aligned. In this paper, we revisit the use of CLIP features for AI-generated image detection and uncover a critical limitation: the high-level semantic information embedded in CLIP's visual features hinders effective discrimination. To address this, we propose NS-Net, a novel detection framework that leverages NULL-Space projection to decouple semantic information from CLIP's visual features, followed by contrastive learning to capture intrinsic distributional differences between real and generated images. Furthermore, we design a Patch Selection strategy to preserve fine-grained artifacts by mitigating semantic bias caused by global image structures. Extensive experiments on an open-world benchmark comprising images generated by 40 diverse generative models show that NS-Net outperforms existing state-of-the-art methods, achieving a 7.4% improvement in detection accuracy, thereby demonstrating strong generalization across both GAN- and diffusion-based image generation techniques.

Problem

• Detecting AI-generated images across unknown models
• Decoupling semantic info from CLIP for better detection
• Improving generalization in GAN and diffusion image detection

Innovation

• NULL-Space projection decouples CLIP semantic information
• Contrastive learning captures real vs generated differences
• Patch Selection mitigates semantic bias in global structures