Model Copyright Protection in Buyer-seller Environment (2312.05262v1)

Abstract: Training a deep neural network (DNN) requires a high computational cost. Buying models from sellers with a large number of computing resources has become prevailing. However, the buyer-seller environment is not always trusted. To protect the neural network models from leaking in an untrusted environment, we propose a novel copyright protection scheme for DNN using an input-sensitive neural network (ISNN). The main idea of ISNN is to make a DNN sensitive to the key and copyright information. Therefore, only the buyer with a correct key can utilize the ISNN. During the training phase, we add a specific perturbation to the clean images and mark them as legal inputs, while the other inputs are treated as illegal input. We design a loss function to make the outputs of legal inputs close to the true ones, while the illegal inputs are far away from true results. Experimental results demonstrate that the proposed scheme is effective, valid, and secure.

• The paper presents an input-sensitive neural network approach that embeds copyright within DNN operation, ensuring only authorized use by intended buyers.
• It employs a methodology that perturbs training inputs and rigorously tests resilience using the ResNet18 model on the CIFAR10 dataset against various attack vectors.
• Experimental results demonstrate a balance between attack resistance and model utility, highlighting optimal perturbation intensities and label-consistent training methods.

Novel Protection Scheme for Deep Neural Network Copyright in Buyer-Seller Environments

Overview of the Proposed Scheme

In an era where deep neural network (DNN) models are deemed valuable software assets, ensuring their security, especially in buyer-seller transactions, is paramount. The paper introduces an innovative copyright protection scheme designed for DNN models, leveraging an input-sensitive neural network (ISNN) concept. This approach uniquely embeds copyright protection directly into the model's operation, making it sensitive to a specific key and copyright information, hence usable solely by the intended buyer. The foundational strategy involves perturbing input images during the training phase, marking them as either legal or illegal inputs depending on their conformity with a predetermined key. This approach effectively safeguards the DNN model against unauthorized use within untrusted environments.

Threat Model and Design Goals

The threat model addresses scenarios where adversaries have complete access to the model's details but not the secretive key. It considers potential attacks like retraining, forging copyright, and reverse iterating attacks, illustrating the extent to which adversaries might go to misuse or misappropriate DNN models. Conversely, the design goals focus on verifiability, low-complexity, flexibility, crypticity, fidelity, effectiveness, and security. These goals outline a framework for evaluating the proposed scheme's ability to deliver and utilize DNNs securely in untrusted environments.

Proposed Methodology

The core methodology revolves around making the model inputs sensitive such that only inputs with a specific perturbation, known to the buyer through a secure key, are considered legal. The paper details a sophisticated process spanning copyright embedding, dataset preprocessing, and model training. This process uniquely intertwines creating a secure method for protecting model copyright without the need for traditional encryption or decryption during inference, hence ensuring both security and practicality.

Experimental Results

The empirical analysis utilized the ResNet18 model over the CIFAR10 dataset, showcasing the scheme's effectiveness against various attack strategies while maintaining the model's utility for legitimate users. Notably:

• The scheme demonstrated resilience against retraining and forging copyright attacks, with the adversary's success rates significantly hindered.
• A distinction is made between label-consistent and label-inconsistent methods, with the former showing an optimal balance between attack resilience and model utility at certain perturbation intensities.
• The experiments underscore the importance of choosing an appropriate perturbation intensity and training method, as these factors critically influence the model's security and fidelity.

Conclusion and Future Implications

The paper presents a robust and novel approach to DNN model copyright protection in buyer-seller environments, addressing a significant need in the AI domain. The proposed ISNN-based scheme offers a promising direction for protecting valuable software commodities without compromising operational efficiency or user experience. Looking forward, this research opens avenues for further exploration into optimizing perturbation strategies, expanding the model applicability across different architectures and use cases, and enhancing the scheme's resistance to increasingly sophisticated attacks. This contribution not only underscores the importance of model security in the contemporary AI landscape but also lays the groundwork for future innovations in digital asset protection.