Latent diffusion models have exhibited considerable potential in generative tasks. Watermarking is considered to be an alternative to safeguard the copyright of generative models and prevent their misuse. However, in the context of model distribution scenarios, the accessibility of models to large scale of model users brings new challenges to the security, efficiency and robustness of existing watermark solutions. To address these issues, we propose a secure and efficient watermarking solution. A new security mechanism is designed to prevent watermark leakage and watermark escape, which considers watermark randomness and watermark-model association as two constraints for mandatory watermark injection. To reduce the time cost of training the security module, watermark injection and the security mechanism are decoupled, ensuring that fine-tuning VAE only accomplishes the security mechanism without the burden of learning watermark patterns. A watermark distribution-based verification strategy is proposed to enhance the robustness against diverse attacks in the model distribution scenarios. Experimental results prove that our watermarking consistently outperforms existing six baselines on effectiveness and robustness against ten image processing attacks and adversarial attacks, while enhancing security in the distribution scenarios.

Vertical Federated Learning (VFL) has garnered significant attention as a privacy-preserving machine learning framework for sample-aligned feature federation. However, traditional VFL approaches do not address the challenges of class and feature continual learning, resulting in catastrophic forgetting of knowledge from previous tasks. To address the above challenge, we propose a novel vertical federated continual learning method, named Vertical Federated Continual Learning via Evolving Prototype Knowledge (V-LETO), which primarily facilitates the transfer of knowledge from previous tasks through the evolution of prototypes. Specifically, we propose an evolving prototype knowledge method, enabling the global model to retain both previous and current task knowledge. Furthermore, we introduce a model optimization technique that mitigates the forgetting of previous task knowledge by restricting updates to specific parameters of the local model, thereby enhancing overall performance. Extensive experiments conducted in both CIL and FIL settings demonstrate that our method, V-LETO, outperforms the other state-of-the-art methods. For example, our method outperforms the state-of-the-art method by 10.39% and 35.15% for CIL and FIL tasks, respectively. Our code is available at https://anonymous.4open.science/r/V-LETO-0108/README.md.

Multimodal Federated Learning (MFL) enables multiple clients to collaboratively train models on multimodal data while ensuring clients' privacy. However, modality and task heterogeneity hinder clients from learning a unified representation, weakening local model generalization, especially in MFL with mixed modalities where only some clients have multimodal data. In this work, we propose an Adaptive prototype-based Multimodal Federated Learning (AproMFL) framework for mixed modalities and heterogeneous tasks to address the aforementioned issues. Our AproMFL transfers knowledge through adaptively-constructed prototypes without a prior public dataset. Clients adaptively select prototype construction methods in line with tasks; server converts client prototypes into unified multimodal prototypes and aggregates them to form global prototypes, avoid clients keeping unified labels. We divide the model into various modules and only aggregate mapping modules to reduce communication and computation overhead. To address aggregation issues in heterogeneity, we develop a client relationship graph-based scheme to dynamically adjust aggregation weights. Extensive experiments on representative datasets evidence effectiveness of AproMFL.

The personalization techniques of diffusion models succeed in generating specific concepts but also pose threats to copyright protection and illegal use. Model Watermarking is an effective method to prevent the unauthorized use of subject-driven or style-driven image generation, safeguarding concept copyrights. However, under the goal of concept-oriented protection, current watermarking schemes typically add watermarks to all images rather than applying them in a refined manner targeted at specific concepts. Additionally, the personalization techniques of diffusion models can easily remove watermarks. Existing watermarking methods struggle to achieve fine-grained watermark embedding with a few images of specific concept and prevent removal of watermarks through personalized fine-tuning. Therefore, we introduce a novel concept-oriented watermarking framework that seamlessly embeds imperceptible watermarks into the concept of diffusion models. We conduct extensive experiments and ablation studies to verify our framework. Our code is available at https://anonymous.4open.science/r/Conceptwm-4EB3/.

Recent booming development of Generative Artificial Intelligence (GenAI) has facilitated an emerging model commercialization for the purpose of reinforcement on model performance, such as licensing or trading Deep Neural Network (DNN) models. However, DNN model trading may trigger concerns of the unauthorized replications or misuses over the model, so that the benefit of the model ownership will be violated. Model identity auditing is a challenging issue in protecting intellectual property of DNN models and verifying the integrity and ownership of models for guaranteeing trusts in transactions is one of the critical obstacles. In this paper, we focus on the above issue and propose a novel Accumulator-enabled Auditing for Distributed Identity of DNN Model (A2-DIDM) that utilizes blockchain and zero-knowledge techniques to protect data and function privacy while ensuring the lightweight on-chain ownership verification. The proposed model presents a scheme of identity records via configuring model weight checkpoints with corresponding zero-knowledge proofs, which incorporates predicates to capture incremental state changes in model weight checkpoints. Our scheme ensures both computational integrity of DNN training process and programmability, so that the uniqueness of the weight checkpoint sequence in a DNN model is preserved, ensuring the correctness of the model identity auditing. In addition, A2-DIDM also addresses privacy protections in distributed identity via a proposed method of accumulators. We systematically analyze the security and robustness of our proposed model and further evaluate the effectiveness and usability of auditing DNN model identities.

Latent Diffusion Models (LDMs) enable a wide range of applications but raise ethical concerns regarding illegal utilization.Adding watermarks to generative model outputs is a vital technique employed for copyright tracking and mitigating potential risks associated with AI-generated content. However, post-hoc watermarking techniques are susceptible to evasion. Existing watermarking methods for LDMs can only embed fixed messages. Watermark message alteration requires model retraining. The stability of the watermark is influenced by model updates and iterations. Furthermore, the current reconstruction-based watermark removal techniques utilizing variational autoencoders (VAE) and diffusion models have the capability to remove a significant portion of watermarks. Therefore, we propose a novel technique called DiffuseTrace. The goal is to embed invisible watermarks in all generated images for future detection semantically. The method establishes a unified representation of the initial latent variables and the watermark information through training an encoder-decoder model. The watermark information is embedded into the initial latent variables through the encoder and integrated into the sampling process. The watermark information is extracted by reversing the diffusion process and utilizing the decoder. DiffuseTrace does not rely on fine-tuning of the diffusion model components. The watermark is embedded into the image space semantically without compromising image quality. The encoder-decoder can be utilized as a plug-in in arbitrary diffusion models. We validate through experiments the effectiveness and flexibility of DiffuseTrace. DiffuseTrace holds an unprecedented advantage in combating the latest attacks based on variational autoencoders and Diffusion Models.

Vertical federated learning has garnered significant attention as it allows clients to train machine learning models collaboratively without sharing local data, which protects the client's local private data. However, existing VFL methods face challenges when dealing with heterogeneous local models among participants, which affects optimization convergence and generalization. To address this challenge, this paper proposes a novel approach called Vertical federated learning for training multiple Heterogeneous models (VFedMH). VFedMH focuses on aggregating the local embeddings of each participant's knowledge during forward propagation. To protect the participants' local embedding values, we propose an embedding protection method based on lightweight blinding factors. In particular, participants obtain local embedding using local heterogeneous models. Then the passive party, who owns only features of the sample, injects the blinding factor into the local embedding and sends it to the active party. The active party aggregates local embeddings to obtain global knowledge embeddings and sends them to passive parties. The passive parties then utilize the global embeddings to propagate forward on their local heterogeneous networks. However, the passive party does not own the sample labels, so the local model gradient cannot be calculated locally. To overcome this limitation, the active party assists the passive party in computing its local heterogeneous model gradients. Then, each participant trains their local model using the heterogeneous model gradients. The objective is to minimize the loss value of their respective local heterogeneous models. Extensive experiments are conducted to demonstrate that VFedMH can simultaneously train multiple heterogeneous models with heterogeneous optimization and outperform some recent methods in model performance.

Distributed machine learning enables parallel training of extensive datasets by delegating computing tasks across multiple workers. Despite the cost reduction benefits of distributed machine learning, the dissemination of final model weights often leads to potential conflicts over model ownership as workers struggle to substantiate their involvement in the training computation. To address the above ownership issues and prevent accidental failures and malicious attacks, verifying the computational integrity and effectiveness of workers becomes particularly crucial in distributed machine learning. In this paper, we proposed a novel binary linear tree commitment-based ownership protection model to ensure computational integrity with limited overhead and concise proof. Due to the frequent updates of parameters during training, our commitment scheme introduces a maintainable tree structure to reduce the costs of updating proofs. Distinguished from SNARK-based verifiable computation, our model achieves efficient proof aggregation by leveraging inner product arguments. Furthermore, proofs of model weights are watermarked by worker identity keys to prevent commitments from being forged or duplicated. The performance analysis and comparison with SNARK-based hash commitments validate the efficacy of our model in preserving computational integrity within distributed machine learning.