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Found 8 papers, 7 papers with code
Understanding Variation in Subpopulation Susceptibility to Poisoning Attacks
Machine learning is susceptible to poisoning attacks, in which an attacker controls a small fraction of the training data and chooses that data with the goal of inducing some behavior unintended by the model developer in the trained model.
SoK: Pitfalls in Evaluating Black-Box Attacks
However, these works make different assumptions on the adversary's knowledge and current literature lacks a cohesive organization centered around the threat model.
Manipulating Transfer Learning for Property Inference
We demonstrate attacks in which an adversary can manipulate the upstream model to conduct highly effective and specific property inference attacks (AUC score $> 0. 9$), without incurring significant performance loss on the main task.
Stealthy Backdoors as Compression Artifacts
In a backdoor attack on a machine learning model, an adversary produces a model that performs well on normal inputs but outputs targeted misclassifications on inputs containing a small trigger pattern.
Model-Targeted Poisoning Attacks with Provable Convergence
Our attack is the first model-targeted poisoning attack that provides provable convergence for convex models, and in our experiments, it either exceeds or matches state-of-the-art attacks in terms of attack success rate and distance to the target model.
Scalable Attack on Graph Data by Injecting Vicious Nodes
Recent studies have shown that graph convolution networks (GCNs) are vulnerable to carefully designed attacks, which aim to cause misclassification of a specific node on the graph with unnoticeable perturbations.
Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries
In a black-box setting, the adversary only has API access to the target model and each query is expensive.
Cryptography and Security
Query-limited Black-box Attacks to Classifiers
Specifically, we consider the problem of attacking machine learning classifiers subject to a budget of feature modification cost while minimizing the number of queries, where each query returns only a class and confidence score.
