When machine learning meets privacy: A survey and outlook
The newly emerged machine learning (eg, deep learning) methods have become a strong
driving force to revolutionize a wide range of industries, such as smart healthcare, financial …
driving force to revolutionize a wide range of industries, such as smart healthcare, financial …
Wild patterns reloaded: A survey of machine learning security against training data poisoning
AE Cinà, K Grosse, A Demontis, S Vascon… - ACM Computing …, 2023 - dl.acm.org
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 …
Poisoning web-scale training datasets is practical
N Carlini, M Jagielski… - … IEEE Symposium on …, 2024 - ieeexplore.ieee.org
Deep learning models are often trained on distributed, web-scale datasets crawled from the
internet. In this paper, we introduce two new dataset poisoning attacks that intentionally …
internet. In this paper, we introduce two new dataset poisoning attacks that intentionally …
Anti-backdoor learning: Training clean models on poisoned data
Backdoor attack has emerged as a major security threat to deep neural networks (DNNs).
While existing defense methods have demonstrated promising results on detecting or …
While existing defense methods have demonstrated promising results on detecting or …
Lira: Learnable, imperceptible and robust backdoor attacks
Recently, machine learning models have demonstrated to be vulnerable to backdoor
attacks, primarily due to the lack of transparency in black-box models such as deep neural …
attacks, primarily due to the lack of transparency in black-box models such as deep neural …
Backdoor learning: A survey
Y Li, Y Jiang, Z Li, ST **-based backdoor attack
With the thriving of deep learning and the widespread practice of using pre-trained networks,
backdoor attacks have become an increasing security threat drawing many research …
backdoor attacks have become an increasing security threat drawing many research …
Reflection backdoor: A natural backdoor attack on deep neural networks
Recent studies have shown that DNNs can be compromised by backdoor attacks crafted at
training time. A backdoor attack installs a backdoor into the victim model by injecting a …
training time. A backdoor attack installs a backdoor into the victim model by injecting a …
Neural attention distillation: Erasing backdoor triggers from deep neural networks
Deep neural networks (DNNs) are known vulnerable to backdoor attacks, a training time
attack that injects a trigger pattern into a small proportion of training data so as to control the …
attack that injects a trigger pattern into a small proportion of training data so as to control the …
Input-aware dynamic backdoor attack
In recent years, neural backdoor attack has been considered to be a potential security threat
to deep learning systems. Such systems, while achieving the state-of-the-art performance on …
to deep learning systems. Such systems, while achieving the state-of-the-art performance on …