alibi-detect

Alibi Detect 是一个源码可用的 Python 库，专注于异常值 、对抗性 和漂移 检测。该包旨在覆盖适用于表格数据、文本、图像和时间序列的在线和离线检测器。漂移检测同时支持 TensorFlow 和 PyTorch 后端。 文档 如需了解更多有关在生产环境中监测异常值和分布的重要性的背景信息，请查看来自 ICML 2020 研讨会 机器学习系统的部署与监测挑战 上的这个演讲，该演讲基于论文生产环境中模型的监测与可解释性并引用了 Alibi Detect。...

SafeClaw-R: Towards Safe and Secure Multi-Agent Personal Assistants

LLM-based multi-agent systems MASs are transforming personal productivity by autonomously executing complex, cross-platform tasks. Frameworks such as OpenClaw demonstrate the potential of locally deployed agents integrated with personal data and services, but this autonomy introduces significant...

Enhancing Security in Deep Reinforcement Learning: A Comprehensive Survey on Adversarial Attacks and Defenses

With the wide application of deep reinforcement learning DRL techniques in complex fields such as autonomous driving, intelligent manufacturing, and smart healthcare, how to improve its security and robustness in dynamic and changeable environments has become a core issue in current research...

A Statistical Method for Attack-Agnostic Adversarial Attack Detection with Compressive Sensing Comparison

Adversarial attacks present a significant threat to modern machine learning systems. Yet, existing detection methods often lack the ability to detect unseen attacks or detect different attack types with a high level of accuracy. In this work, we propose a statistical approach that establishes a...

Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples

Adversarial detection protects models from adversarial attacks by refusing suspicious test samples. However, current detection methods often suffer from weak generalization: their effectiveness tends to degrade significantly when applied to adversarially trained models rather than naturally train...