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Found 6 papers, 5 papers with code
LMEraser: Large Model Unlearning through Adaptive Prompt Tuning
To address the growing demand for privacy protection in machine learning, we propose a novel and efficient machine unlearning approach for \textbf{L}arge \textbf{M}odels, called \textbf{LM}Eraser.
Machine Unlearning: Solutions and Challenges
Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious data, posing risks of privacy breaches, security vulnerabilities, and performance degradation.
Intellectual Property Protection of Diffusion Models via the Watermark Diffusion Process
This paper introduces WDM, a novel watermarking solution for diffusion models without imprinting the watermark during task generation.
SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service
With the proliferation of cloud computing, it is increasingly popular to deploy the services of complex and resource-intensive model training and inference in the cloud due to its prominent benefits.
Detecting and Identifying Optical Signal Attacks on Autonomous Driving Systems
In our study, we use real data sets and the state-of-the-art machine learning model to evaluate our attack detection scheme and the results confirm the effectiveness of our detection method.
Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles
In this paper, we investigate the impact of two primary types of adversarial attacks, perturbation attacks and patch attacks, on the driving safety of vision-based autonomous vehicles rather than the detection precision of deep learning models.
