This work provides the community with a timely comprehensive review of backdoor attacks
and countermeasures on deep learning. According to the attacker's capability and affected …

Deep Learning algorithms have achieved state-of-the-art performance for Image
Classification. For this reason, they have been used even in security-critical applications …

As deep neural networks (DNNs) are growing larger, their requirements for computational
resources become huge, which makes outsourcing training more popular. Training in a third …

This paper presents a technique to scan neural network based AI models to determine if
they are trojaned. Pre-trained AI models may contain back-doors that are injected through …

As machine learning systems grow in scale, so do their training data requirements, forcing
practitioners to automate and outsource the curation of training data in order to achieve state …

In machine learning Trojan attacks, an adversary trains a corrupted model that obtains good
performance on normal data but behaves maliciously on data samples with certain trigger …

Backdoor attacks aim to cause misclassification of a subject model by stamping a trigger to
inputs. Backdoors could be injected through malicious training and naturally exist. Deriving …

In recent years, generative adversarial networks (GANs) and its variants have achieved
unprecedented success in image synthesis. They are widely adopted in synthesizing facial …

Backdoor attacks embed hidden malicious behaviors into deep learning models, which only
activate and cause misclassifications on model inputs containing a specific" trigger." Existing …

Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …