NAU-QMUL: Utilizing BERT and CLIP for Multi-modal AI-Generated Image Detection

• URL: http://arxiv.org/abs/2602.23863v1
• Date: Fri, 27 Feb 2026 10:03:54 GMT
• Title: NAU-QMUL: Utilizing BERT and CLIP for Multi-modal AI-Generated Image Detection
• Authors: Xiaoyu Guo, Arkaitz Zubiaga,
• Abstract summary: We propose a multi-modal multi-task model for detecting AI-generated images.<n>The model achieved fifth place in both Tasks A and B of the CT2: AI-Generated Image Detection' competition.<n>These findings highlight the effectiveness of the proposed architecture and its potential for advancing AI-generated content detection in real-world scenarios.
• Score: 16.276161463898934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the aim of detecting AI-generated images and identifying the specific models responsible for their generation, we propose a multi-modal multi-task model. The model leverages pre-trained BERT and CLIP Vision encoders for text and image feature extraction, respectively, and employs cross-modal feature fusion with a tailored multi-task loss function. Additionally, a pseudo-labeling-based data augmentation strategy was utilized to expand the training dataset with high-confidence samples. The model achieved fifth place in both Tasks A and B of the `CT2: AI-Generated Image Detection' competition, with F1 scores of 83.16\% and 48.88\%, respectively. These findings highlight the effectiveness of the proposed architecture and its potential for advancing AI-generated content detection in real-world scenarios. The source code for our method is published on https://github.com/xxxxxxxxy/AIGeneratedImageDetection.

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