Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2026

Abstract

Rapid advances in AI-generated image (AIGI) technology enable highly realistic synthesis, threatening public information integrity and security. Recent studies have demonstrated that incorporating texture-level artifact features alongside semantic features into multimodal large language models (MLLMs) can enhance their AIGI detection capability. However, our preliminary analyses reveal that artifact features exhibit high intra-feature similarity, leading to an almost uniform attention map after the softmax operation. This phenomenon causes attention dilution, thereby hindering effective fusion between semantic and artifact features. To overcome this limitation, we propose a lightweight fusion adapter, TranX-Adapter, which integrates a Task-aware Optimal-Transport Fusion that leverages the Jensen-Shannon divergence between artifact and semantic prediction probabilities as a cost matrix to transfer artifact information into semantic features, and an X-Fusion that employs cross-attention to transfer semantic information into artifact features. Experiments on standard AIGI detection benchmarks upon several advanced MLLMs, show that our TranXAdapter brings consistent and significant improvements (up to +6% accuracy). Code will be available at https://github.com/DreamMr/ TranX-Adapter.

Discipline

Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

Proceedings of the 43rd International Conference on Machine Learning (ICML 2026), Seoul, South Korea, July 6-11

First Page

1

Last Page

11

Publisher

IMLS

City or Country

Seoul, Korea

Additional URL

https://icml.cc/virtual/2026/poster/64870

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