Recent success of deep learning is largely attributed to the sheer amount of data used for
training deep neural networks. Despite the unprecedented success, the massive data …

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 …

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 …

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 …

The prosperity of deep neural networks (DNNs) is largely benefited from open-source
datasets, based on which users can evaluate and improve their methods. In this paper, we …

In a backdoor attack, an adversary injects corrupted data into a model's training dataset in
order to gain control over its predictions on images with a specific attacker-defined trigger. A …

Deep neural networks are vulnerable to Trojan attacks. Existing attacks use visible patterns
(eg, a patch or image transformations) as triggers, which are vulnerable to human …

Recent studies have shown that Deep Neural Networks (DNNs) are vulnerable to the
backdoor attacks, which leads to malicious behaviors of DNNs when specific triggers are …

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 …

Abstract Deep Neural Networks are vulnerable to Trojan (or backdoor) attacks. Reverse-
engineering methods can reconstruct the trigger and thus identify affected models. Existing …