Attacking machine learning with adversarial examples

A New Type of Adversarial Examples

The paper introduces a new class of adversarial examples that are markedly different from original inputs yet produce the same model output. It presents algorithms such as NI-FGSM, NI-FGM, and their momentum variants (NMI-FGSM, NMI-FGM) to generate these examples. The authors demonstrate that these adversarial examples are not confined to the vicinity of training data but are spread throughout the sample space.

Position: Adversarial ML for LLMs Is Not Making Any Progress

arXiv:2502. 02260v2 Announce Type: replace Abstract: In the past decade, considerable research effort has been devoted to securing machine learning (ML) models that operate in adversarial settings.

Statistical Adversaries: Natural Backdoor-like Features in Vision Datasets

Model-specific adversarial attacks have been extensively studied. We study a different failure mode: naturally occurring statistical signals in vision data that can behave like backdoor-like triggers without being maliciously inserted.

Merge Now, Regret Later: The Hidden Cost of Model Merging Is Adversarial Transferability

arXiv:2509. 23689v2 Announce Type: replace Abstract: Model Merging (MM) has proven to be an effective alternative to multi-task learning, where several fine-tuned models are merged, without access to the tasks' training data, into one model that retains performance across different tasks.

Adversarial attacks on neural network policies

Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis

arXiv:2406. 10090v3 Announce Type: replace Abstract: Thanks to their extensive capacity, over-parameterized neural networks exhibit superior predictive capabilities and generalization.