Aug 11, 2023 · We propose to tackle the task with a much simpler paradigm. Specifically, we steal a data set with GAN before training the clone model rather ...

Combining the above strategies, we propose an efficient model extraction by dataset stealing, balancing, and filtering(DSBF). Experiments on three widely ...

Combining the above strategies, we propose an efficient model extraction by dataset stealing, balancing, and filtering(DSBF). Experiments on three widely-used ...

Missing: Filtering. | Show results with:Filtering.

Sep 16, 2024 · Our evaluation shows that CaBaGE outperforms existing techniques on seven datasets—MNIST, FMNIST, SVHN, CIFAR-10, CIFAR-100, ImageNet-subset, ...

Model extraction is the process of training a local substitute model with a substitute dataset that is annotated by the target models that need to be extracted.

Feb 2, 2023 · Ref. [13] proposed a cloud-based extraction monitor that quantifies the extraction status of a model by observing the query and response streams ...

The technique of model stealing (also called “model extraction”) aims at obtaining, e.g., training hyperparameters, the model architecture, learned parameters, ...

This work proposes an efficient model extraction by data set stealing, balancing, and filtering (DSBF), which outperforms previous methods while converging ...

In this paper, we propose a novel and practical paradigm to extract private data from the pre-trained LLMs via a stealthy backdoor injection. As shown in Figure ...