The exceptional performance of diffusion models establishes them as high-value intellectual property but exposes them to unauthorized replication. Existing protection methods either modify the model to embed watermarks, which impairs performance, or extract model fingerprints by manipulating the denoising process, rendering them incompatible with black-box APIs. In this paper, we propose TrajPrint, a completely lossless and training-free framework that verifies model copyright by extracting unique manifold fingerprints formed during deterministic generation. Specifically, we first utilize a watermarked image as an anchor and exactly trace the path back to its trajectory origin, effectively locking the model fingerprint mapped by this path. Subsequently, we implement a joint optimization strategy that employs dual-end anchoring to synthesize a specific fingerprint noise, which strictly adheres to the target manifold for robust watermark recovery. As input, it enables the protected target model to recover the watermarked image, while failing on non-target models. Finally, we achieved verification via atomic inference and statistical hypothesis testing. Extensive experiments demonstrate that TrajPrint achieves lossless verification in black-box API scenarios with superior robustness against model modifications.
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）
Gaussian Shading++: Rethinking the Realistic Deployment Challenge of Performance-Lossless Image Watermark for Diffusion Models
University of Science and Technology of China（中国科学技术大学）；Anhui Province Key Laboratory of Digital Security（安徽省数字安全重点实验室）；Department of Information Engineering and Mathematics（信息工程与数学系）；University of Siena（锡耶纳大学）；School of Computing（计算学院）；National University of Singapore（新加坡国立大学）
Efficient, Robust, and Anti-Collusion Fingerprinting of Image Diffusion Models
University of Florence（佛罗伦萨大学）；Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；College of Cyber Security, Jinan University（暨南大学网络空间安全学院）；State Key Laboratory of Internet of Things for Smart City, University of Macau（澳门大学智慧城市物联网国家重点实验室）；Department of Computer and Information Science, Faculty of Science and Technology, University of Macau（澳门大学科技学院计算机与信息科学系）；University of Siena（锡耶纳大学）
SoK: Large Language Model Copyright Auditing via Fingerprinting
The State Key Laboratory of Blockchain and Data Security（区块链与数据安全国家重点实验室）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江）区块链与数据安全研究院）；Nanyang Technological University（南洋理工大学）；City University of Hong Kong（香港城市大学）；Sun Yat-Sen University（中山大学）
Deep neural networks (DNNs) have already achieved great success in a lot of application areas and brought profound changes to our society. However, it also raises new security problems, among which how to protect the intellectual property (IP) of DNNs against infringement is one of the most important yet very challenging topics. To deal with this problem, recent studies focus on the IP protection of DNNs by applying digital watermarking, which embeds source information and/or authentication data into DNN models by tuning network parameters directly or indirectly. However, tuning network parame…
SKeDA: A Generative Watermarking Framework for Text-to-video Diffusion Models
School of Electronic and Information Engineering, Anhui University（安徽大学电子与信息工程学院）；Anhui Province Key Laboratory of Digital Security and the CAS Key Laboratory of Electromagnetic Space Information, University of Science and Technology of China（安徽省数字安全重点实验室和中国科学院电磁空间信息重点实验室，中国科学技术大学）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）
Deep learning (DL) models, especially those large-scale and high-performance ones, can be very costly to train, demanding a great amount of data and computational resources. Unauthorized reproduction of DL models can lead to copyright infringement and cause huge economic losses to model owners. Existing copyright protection techniques are mostly based on watermarking, which embeds an owner-specified watermark into the model. While being able to provide exact ownership verification, these techniques are 1) invasive, as they need to tamper with the training process, which may affect the utility …
GoodDiffusion: Proactive Copyright Protection for Diffusion Bridge Models via Learnable Sample-specific Signatures
School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所人工智能安全全国重点实验室）；Beijing Academy of Artificial Intelligence (BAAI)（北京智源人工智能研究院）
Protecting Deep Learning Model Copyrights with Adversarial Example-Free Reuse Detection
School of Mathematical Sciences, Peking University, China（北京大学数学科学学院）；School of Computer Science, University of Bristol, UK（英国布里斯托大学计算机科学学院）；School of Control Science and Engineering, Zhejiang University, China（浙江大学控制科学与工程学院）
提出 NFARD，利用神经元功能与正常样本检测 DNN 复用关系，支持白盒、黑盒及异构场景，并构建 Reuse Zoo，检测 F1 最高达 1.0。
