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 …

Recent advances in artificial intelligence (AI) have significantly intensified research in the
geoscience and remote sensing (RS) field. AI algorithms, especially deep learning-based …

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 …

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 …

While existing backdoor attacks have successfully infected multimodal contrastive learning
models such as CLIP they can be easily countered by specialized backdoor defenses for …

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 …

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 …

Backdoor learning is an emerging and vital topic for studying deep neural networks'
vulnerability (DNNs). Many pioneering backdoor attack and defense methods are being …

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 …

Recently, backdoor attacks pose a new security threat to the training process of deep neural
networks (DNNs). Attackers intend to inject hidden backdoors into DNNs, such that the …