AggMo-Enhanced Momentum Attack: A Plug-and-Play Framework for Boosting Adversarial Transferability

Most adversarial attack methods achieve high success rates under the white-box setting. However, these methods often lack transferability when targeting other deep neural network (DNN) models. Momentum-based attacks have emerged as an effective strategy to enhance transferability by incorporating a momentum term to stabilize update directions. While simple constant-momentum methods (e.g., MI-FGSM) or advanced variants (e.g., NI-FGSM, VMI-FGSM) have shown promise, they either use a single momentum decay factor or introduce significant computational overhead. To address this, we propose a novel plug-and-play momentum aggregation framework named AggMo-Attack. Our key insight is that a single momentum term with a fixed decay factor cannot optimally capture the multi-scale temporal correlations in gradients during adversarial optimization. Inspired by the Aggregated Momentum (AggMo) optimizer, we designed a multi-momentum aggregation module that maintains and weightedly combines multiple velocity vectors with different decay factors. This framework can be seamlessly integrated into existing momentum-based attack methods (e.g., MI-FGSM, NI-FGSM, VMI-FGSM) as a drop-in replacement for their standard momentum update step. Extensive experiments demonstrate that integrating our AggMo module significantly improves adversarial transferability. Our work provides a versatile and effective tool for enhancing momentum-based adversarial attacks and opens a new direction for designing adaptive attack strategies.