(Post by Sean Miller, using images adapted from Suya’s talk slides)
Data Poisoning Attacks Machine learning models are often trained using data from untrusted sources, leaving them open to poisoning attacks where adversaries use their control over a small fraction of that training data to poison the model in a particular way.
Most work on poisoning attacks is directly driven by an attacker’s objective, where the adversary chooses poisoning points that maximize some target objective.
New: Video Presentation
Finding Black-box Adversarial Examples with Limited Queries Black-box attacks generate adversarial examples (AEs) against deep neural networks with only API access to the victim model.
Existing black-box attacks can be grouped into two main categories:
Transfer Attacks use white-box attacks on local models to find candidate adversarial examples that transfer to the target model.
Optimization Attacks use queries to the target model and apply optimization techniques to search for adversarial examples.