TD-CAG: Enhancing adversarial transferability via curvature awareness and spatial dislocation.

Abstract

Transfer-based adversarial attacks are widely used to evaluate the robustness of deep neural networks (DNNs) under black-box settings, yet improving their cross-model transferability remains a key challenge. This limitation arises from reliance on locally linear gradient approximations and insufficient guidance from semantically critical regions. To address these limitations, we propose Translation-Dislocation and Curvature-Aware Gradient (TD-CAG), a new adversarial attack framework comprising two complementary modules: a curvature-aware gradient (CAG) module for modeling local nonlinearity, and a translation-dislocation (TD) module for modeling saliency misalignment. Specifically, CAG approximates second-order directional curvature to help perturbations escape local linearity, and TD introduces lightweight spatial shifts to simulate saliency misalignments across architectures, guiding perturbations toward model-invariant discriminative regions. Both modules are plug-and-play and compatible with existing attack pipelines. Extensive experiments demonstrate that TD-CAG consistently achieves superior transferability compared to state-of-the-art attack methods, while maintaining high compatibility and low computational cost. The source code is publicly available at https://github.com/hlkuang24/TD-CAG.