# General and Domain-Specific Zero-shot Detection of Generated Images via Conditional Likelihood

arXiv: https://arxiv.org/abs/2512.05590 · HTML: https://mlsanity.com/paper/2512.05590/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2025.

## Built on by (most cited first, 5 of 5)

- [InfoNCE Induces Gaussian Distribution](https://mlsanity.com/paper/2602.24012/) (2026)
- [Training-free Detection of Generated Videos via Spatial-Temporal Likelihoods](https://mlsanity.com/paper/2603.15026/) (2026)
- [The Universal Normal Embedding](https://mlsanity.com/paper/2603.21786/) (2026)
- [Make it SING: Analyzing Semantic Invariants in Classifiers](https://mlsanity.com/paper/2603.14610/) (2026)
- [Whitening Inverts the Hierarchy: What the Norm of a Whitened Embedding Measures](https://mlsanity.com/paper/2609.23117/) (2026)

## Abstract (arXiv)

The rapid advancement of generative models, particularly diffusion-based methods, has significantly improved the realism of synthetic images. As new generative models continuously emerge, detecting generated images remains a critical challenge. While fully supervised, and few-shot methods have been proposed, maintaining an updated dataset is time-consuming and challenging. Consequently, zero-shot methods have gained increasing attention in recent years. We find that existing zero-shot methods often struggle to adapt to specific image domains, such as artistic images, limiting their real-world applicability. In this work, we introduce CLIDE, a novel zero-shot detection method based on conditional likelihood approximation. Our approach computes likelihoods conditioned on real images, enabling adaptation across diverse image domains. We extensively evaluate CLIDE, demonstrating SOTA performance on a large-scale general dataset and significantly outperform existing methods in domain-specific cases. These results demonstrate the robustness of our method and underscore the need of broad, domain-aware generalization for the AI-generated image detection task. Code is available at https://tinyurl.com/clide-detector.