Backdoor learning: A survey
Backdoor attack intends to embed hidden backdoors into deep neural networks (DNNs), so
that the attacked models perform well on benign samples, whereas their predictions will be …
that the attacked models perform well on benign samples, whereas their predictions will be …
Narcissus: A practical clean-label backdoor attack with limited information
Backdoor attacks introduce manipulated data into a machine learning model's training set,
causing the model to misclassify inputs with a trigger during testing to achieve a desired …
causing the model to misclassify inputs with a trigger during testing to achieve a desired …
Revisiting the assumption of latent separability for backdoor defenses
Recent studies revealed that deep learning is susceptible to backdoor poisoning attacks. An
adversary can embed a hidden backdoor into a model to manipulate its predictions by only …
adversary can embed a hidden backdoor into a model to manipulate its predictions by only …
Badchain: Backdoor chain-of-thought prompting for large language models
Large language models (LLMs) are shown to benefit from chain-of-thought (COT) prompting,
particularly when tackling tasks that require systematic reasoning processes. On the other …
particularly when tackling tasks that require systematic reasoning processes. On the other …
Clean-image backdoor: Attacking multi-label models with poisoned labels only
Multi-label models have been widely used in various applications including image
annotation and object detection. The fly in the ointment is its inherent vulnerability to …
annotation and object detection. The fly in the ointment is its inherent vulnerability to …
Towards a proactive {ML} approach for detecting backdoor poison samples
Adversaries can embed backdoors in deep learning models by introducing backdoor poison
samples into training datasets. In this work, we investigate how to detect such poison …
samples into training datasets. In this work, we investigate how to detect such poison …
A Comprehensive Survey on Backdoor Attacks and their Defenses in Face Recognition Systems
Deep learning has significantly transformed face recognition, enabling the deployment of
large-scale, state-of-the-art solutions worldwide. However, the widespread adoption of deep …
large-scale, state-of-the-art solutions worldwide. However, the widespread adoption of deep …
Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities
Robotics and Artificial Intelligence (AI) have been inextricably intertwined since their
inception. Today, AI-Robotics systems have become an integral part of our daily lives, from …
inception. Today, AI-Robotics systems have become an integral part of our daily lives, from …
Backdoor attacks to deep learning models and countermeasures: A survey
Backdoor attacks have severely threatened deep neural network (DNN) models in the past
several years. In backdoor attacks, the attackers try to plant hidden backdoors into DNN …
several years. In backdoor attacks, the attackers try to plant hidden backdoors into DNN …
LoneNeuron: a highly-effective feature-domain neural trojan using invisible and polymorphic watermarks
The wide adoption of deep neural networks (DNNs) in real-world applications raises
increasing security concerns. Neural Trojans embedded in pre-trained neural networks are …
increasing security concerns. Neural Trojans embedded in pre-trained neural networks are …