8 accepted papers
One prerequisite for secure and reliable artificial intelligence services is tracing the copyright of backend deep neural networks. In the black-box scenario, the copyright of deep neural networks can be traced by their fingerprints, i.e., their outputs on a series of fingerprinting triggers. The…
Model extraction aims to steal a functionally similar copy from a machine learning as a service (MLaaS) API with minimal overhead, typically for illicit profit or as a precursor to further attacks, posing a significant threat to the MLaaS ecosystem. However, recent studies have shown that model extr…
Modern over-parameterized deep models are highly data-dependent, with large scale general-purpose and domain-specific datasets serving as the bedrock for rapid advancements. However, many datasets are proprietary or contain sensitive information, making unrestricted model training problematic. In th…
Fingerprints characterize deep neural networks that are deployed as black-boxes. To achieve copyright tracing and integrity verification, fingerprints are categorized into robust fingerprints and fragile fingerprints. Despite of their distinct motivations, we show that both kinds of neural network f…
Large language models (LLMs) have exhibited great potential in autonomously completing tasks across real-world applications. Despite this, these LLM agents introduce unexpected safety risks when operating in interactive environments. Instead of centering on the harmlessness of LLM-generated content…
To trace the copyright of deep neural networks, an owner can embed its identity information into its model as a watermark. The capacity of the watermark quantify the maximal volume of information that can be verified from the watermarked model. Current studies on capacity focus on the ownership veri…
Federated learning (FL) has enabled global model training on decentralized data in a privacy-preserving way. However, for tasks that utilize pre-trained language models (PLMs) with massive parameters, there are considerable communication costs. Prompt tuning, which tunes soft prompts without modifyi…
The huge training overhead, considerable commercial value, and various potential security risks make it urgent to protect the intellectual property (IP) of Deep Neural Networks (DNNs). DNN watermarking has become a plausible method to meet this need. However, most of the existing watermarking scheme…