The use of artificial intelligence (AI) is becoming more prevalent across industries such as
healthcare, finance, and transportation. Artificial intelligence is based on the analysis of …

The prosperity of machine learning has been accompanied by increasing attacks on the
training process. Among them, poisoning attacks have become an emerging threat during …

When trying to gain better visibility into a machine learning model in order to understand and
mitigate the associated risks, a potentially valuable source of evidence is: which training …

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 …

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 …

We investigate a new method for injecting backdoors into machine learning models, based
on compromising the loss-value computation in the model-training code. We use it to …

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 …

Federated Learning (FL) allows multiple participants to train machine learning models
collaboratively by keeping their datasets local while only exchanging model updates. Alas …

Adversarial attacks alter NLP model predictions by perturbing test-time inputs. However, it is
much less understood whether, and how, predictions can be manipulated with small …

Property inference attacks consider an adversary who has access to a trained ML model and
tries to extract some global statistics of the training data. In this work, we study property …