Papers by Xiaofan Zheng
UMMF: Protecting Copyright of Large Vision-Language Models through Unlearning-based Multimodal Memorization Fingerprint (2026.acl-long)
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| Challenge: | Existing methods for fingerprinting large vision-Language Models rely on explicit triggers, which have limitations in terms of stealthiness and robustness. |
| Approach: | They propose to use model fingerprints to verify the ownership of large vision-Language Models (LVLMs) they use implicit model fingerprinting techniques that leverage neighboring samples as implicit model . |
| Outcome: | The proposed fingerprinting technique is superior to existing methods, but has limitations in terms of stealthiness and robustness. |
Ghost in the Shell: Synonym-Aware Logit Shaping Fingerprint for Copyright Protection of Large Vision-Language Models (2026.findings-acl)
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| Challenge: | Existing fingerprinting methods for large vision-language models rely on backdoors to elicit abnormal outputs, but direct distortion of the model’s original outputs compromises modality alignment and degrades multimodal capabilities. |
| Approach: | They propose to embed a robust fingerprint while preserving the original normal outputs of the model. |
| Outcome: | The proposed fingerprint maintains multimodal performance and substantially enhances fingerprint robustness. |
Unveiling Fake News with Adversarial Arguments Generated by Multimodal Large Language Models (2025.coling-main)
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| Challenge: | Existing methods for detecting fake news rely on neural networks to learn latent feature representations with limited real-world understanding. |
| Approach: | They propose a method that leverages Multimodal Large Language Models for fake news detection that introduces adversarial reasoning through debates from opposing perspectives. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on four fake news detection datasets. |
Tracing Training Footprints: A Calibration Approach for Membership Inference Attacks Against Multimodal Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing methods to improve difficulty calibration for Multimodal Large Language Models only consider text input . visual embeddings in training data reduce effectiveness of these methods . |
| Approach: | They propose a method to detect member samples in poorly generalized local manifolds by visual embeddings. |
| Outcome: | The proposed method surpasses existing methods. |