A comprehensive survey on poisoning attacks and countermeasures in machine learning
The prosperity of machine learning has been accompanied by increasing attacks on the
training process. Among them, poisoning attacks have become an emerging threat during …
training process. Among them, poisoning attacks have become an emerging threat during …
Trustworthy AI: From principles to practices
The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …
of various systems based on it. However, many current AI systems are found vulnerable to …
Trustworthy llms: a survey and guideline for evaluating large language models' alignment
Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …
Data collection and quality challenges in deep learning: A data-centric ai perspective
Data-centric AI is at the center of a fundamental shift in software engineering where machine
learning becomes the new software, powered by big data and computing infrastructure …
learning becomes the new software, powered by big data and computing infrastructure …
A survey on security and privacy of federated learning
Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon
decentralized data and training that brings learning to the edge or directly on-device. FL is a …
decentralized data and training that brings learning to the edge or directly on-device. FL is a …
Attack of the tails: Yes, you really can backdoor federated learning
Due to its decentralized nature, Federated Learning (FL) lends itself to adversarial attacks in
the form of backdoors during training. The goal of a backdoor is to corrupt the performance …
the form of backdoors during training. The goal of a backdoor is to corrupt the performance …
Advances and open problems in federated learning
Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …
devices or whole organizations) collaboratively train a model under the orchestration of a …
Local model poisoning attacks to {Byzantine-Robust} federated learning
In federated learning, multiple client devices jointly learn a machine learning model: each
client device maintains a local model for its local training dataset, while a master device …
client device maintains a local model for its local training dataset, while a master device …
Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Lack of transparency in deep neural networks (DNNs) make them susceptible to backdoor
attacks, where hidden associations or triggers override normal classification to produce …
attacks, where hidden associations or triggers override normal classification to produce …
Wild patterns reloaded: A survey of machine learning security against training data poisoning
The success of machine learning is fueled by the increasing availability of computing power
and large training datasets. The training data is used to learn new models or update existing …
and large training datasets. The training data is used to learn new models or update existing …