Echoes of ownership: Adversarial-guided dual injection for copyright protection in MLLMs
School of Information Science and Engineering, Lanzhou University（兰州大学信息科学与工程学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Chengwei Xia, Fan Ma, Ruijie Quan, Yunqiu Xu, Kun Zhan, Yi Yang
With the rapid deployment of multimodal large language models (MLLMs), disputes regarding model ownership have become increasingly frequent, raising significant concerns about intellectual property protection. In this paper, we propose a framework for generating copyright triggers for MLLMs, enabling model publishers to embed verifiable ownership information into the model. The goal is to construct trigger images that elicit ownership-related textual responses exclusively in fine-tuned derivatives, while remaining inert in other non-derivative models. Our method constructs a tracking trigger image by treating the image as a learnable tensor, performing adversarial optimization with dual-injection of ownership-relevant semantic information. The first injection is achieved by enforcing textual consistency between the output of an auxiliary MLLM and a predefined ownership-relevant target text; the consistency loss is backpropagated to inject this ownership-related information into the image. The second injection is performed at the semantic-level by minimizing the distance between the CLIP features of the image and those of the target text. Furthermore, we introduce an additional adversarial training stage involving the auxiliary model. It is specifically trained to resist generating ownership-relevant target text, thereby enhancing robustness in heavily fine-tuned derivative models. Extensive experiments demonstrate the effectiveness of our dual-injection approach in tracking model lineage under various fine-tuning and domain-shift scenarios. Code is at https://github.com/kunzhan/AGDI
Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Hand
University of Maryland（马里兰大学）；ZJU-Hangzhou Global Scientific and Technological Innovation Center（浙江大学杭州国际科创中心）；Zhejiang University（浙江大学）；Northwestern University（西北大学）；Tsinghua University（清华大学）；UC Riverside（加州大学河滨分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Chicago（芝加哥大学）
Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal
University of Chinese Academy of Sciences（中国科学院大学）；Sun Yat-sen University（中山大学）；Institute of Information Engineering, CAS（中国科学院信息工程研究所）；Ant Group（蚂蚁集团）；Tencent Security Zhuque Lab（腾讯安全朱雀实验室）；University of Munich（慕尼黑大学）
Nanyang Technological University（南洋理工大学）；City University of Hong Kong（香港城市大学）；Tsinghua University（清华大学）；Chongqing University（重庆大学）；Wuhan University（武汉大学）；Zhejiang University（浙江大学）；University of New Brunswick（新不伦瑞克大学）
