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Found 13 papers, 6 papers with code
Exploring Incremental Unlearning: Techniques, Challenges, and Future Directions
The growing demand for data privacy in Machine Learning (ML) applications has seen Machine Unlearning (MU) emerge as a critical area of research.
LiveVal: Time-aware Data Valuation via Adaptive Reference Points
Time-aware data valuation enhances training efficiency and model robustness, as early detection of harmful samples could prevent months of wasted computation.
The Hidden Dimensions of LLM Alignment: A Multi-Dimensional Safety Analysis
We then measure how different directions promote or suppress the dominant direction, showing the important role of secondary directions in shaping the model's refusal representation.
CAT: Contrastive Adversarial Training for Evaluating the Robustness of Protective Perturbations in Latent Diffusion Models
In this paper, we first reveal that the primary reason adversarial examples are effective as protective perturbations in latent diffusion models is the distortion of their latent representations, as demonstrated through qualitative and quantitative experiments.
Protective Perturbations against Unauthorized Data Usage in Diffusion-based Image Generation
Diffusion-based text-to-image models have shown immense potential for various image-related tasks.
Embedding Watermarks in Diffusion Process for Model Intellectual Property Protection
In practical application, the widespread deployment of diffusion models often necessitates substantial investment in training.
Training with Differential Privacy: A Gradient-Preserving Noise Reduction Approach with Provable Security
Deep learning models have been extensively adopted in various regions due to their ability to represent hierarchical features, which highly rely on the training set and procedures.
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.
