Fingerprinting Multimodal Large Language Models
While multimodal large language models (MLLMs) enable a wide range of image-text reasoning tasks, recent incidents indicate that they are vulnerable to illicit deployment and unauthorized distillation. Existing solutions for model provenance are typically confounded by shared language backbones in MLLMs and struggle to detect violations of distillation. To bridge this gap and safeguard model ownership, we present the first study on multimodal model fingerprinting. Inspired by recent findings that self-attention acts as a low-pass filter and that its low-frequency components are informative, we develop AttnPrint for white-box provenance. Specifically, we extract cross-modal attention distr...
| Authors | Chao Huang, Meng Tong, Kejiang Chen |
|---|---|
| Primary category | cs.CR |
| Published | 17 September 2026 |
| Last updated at source | 17 September 2026 |
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