Deep Neural Networks are vulnerable toTrojan (or backdoor) attacks.

The Information Bottleneck (IB) objective uses information theory to formulate a task-performance versus robustness trade-off.

Traditional solutions prioritizing easy or hard samples lack the flexibility to handle such a variety simultaneously. Recent work has proposed a more reasonable data selection principle by examining the data's impact on the model's generalization loss.