LHSDet: High-Resolution AI-Generated Image Detection via Visual Question Answering

• URL: http://arxiv.org/abs/2608.07863v1
• Date: Sat, 08 Aug 2026 02:13:33 GMT
• Title: LHSDet: High-Resolution AI-Generated Image Detection via Visual Question Answering
• Abstract summary: We propose a novel high-resolution AI-generated image detector, termed LHSDet.<n>We formulate the AI-generated image detection task as a Visual Question Answering problem.<n>We use a fine-tuned vision-language framework to fully exploit the complementary information between visual and textual modalities.
• Score: 9.47286721599147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Driven by advances in diffusion models and autoregressive models, the fidelity and resolution of AI-generated images now rival those of real images. However, existing AI-generated image detection methods often downsample the images, inevitably overlooking critical low-level texture details in high-resolution AI-generated images, therefore limiting their detection performance. In addition, the ceaseless emergence of unknown generative models makes large-scale pre-training datasets inaccessible. To address these challenges, we propose a novel high-resolution AI-generated image detector, termed LHSDet. Specifically, we formulate the AI-generated image detection task as a Visual Question Answering problem, leveraging a fine-tuned vision-language framework to fully exploit the complementary information between visual and textual modalities. Recognizing that the default visual encoder of existing vision-language models is not tailored for AI-generated image detection, we redesign a visual encoder to better capture both the low-level and high-level artifacts inherent in AI-generated images. Furthermore, we incorporate a semantic-level textual branch to enable multi-modal feature fusion and detection. Consequently, LHSDet employs a triple-branch architecture to extract complementary multi-modal features: a low-level visual branch that aggregates non-overlapping patches for local texture cues, a high-level visual branch based on SigLIP2 for global perception feature extraction, and a semantic-level textual branch that generates captions using BLIP-2. Extensive experimental results demonstrate that LHSDet achieves high detection accuracy and robust performance across diverse generative models, including both diffusion and autoregressive models.

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