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Found 5 papers, 3 papers with code
Poisoning $\times$ Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks
It is well-known that deep learning models are vulnerable to small input perturbations.
Detecting ChatGPT: A Survey of the State of Detecting ChatGPT-Generated Text
While recent advancements in the capabilities and widespread accessibility of generative language models, such as ChatGPT (OpenAI, 2022), have brought about various benefits by generating fluent human-like text, the task of distinguishing between human- and large language model (LLM) generated text has emerged as a crucial problem.
SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detection
Split learning enables efficient and privacy-aware training of a deep neural network by splitting a neural network so that the clients (data holders) compute the first layers and only share the intermediate output with the central compute-heavy server.
UnSplit: Data-Oblivious Model Inversion, Model Stealing, and Label Inference Attacks Against Split Learning
(1) We show that an honest-but-curious split learning server, equipped only with the knowledge of the client neural network architecture, can recover the input samples and obtain a functionally similar model to the client model, without being detected.
SplitGuard: Detecting and Mitigating Training-Hijacking Attacks in Split Learning
Distributed deep learning frameworks such as split learning provide great benefits with regards to the computational cost of training deep neural networks and the privacy-aware utilization of the collective data of a group of data-holders.
