Boosting Transferability of Adversarial Examples with Spatio-Temporal Context

Transferable adversarial examples have received increasing attention for their utility in spoofing multiple models, but existing attacks still perform poorly in terms of transferability. In light of this, a novel attack method called Spatio-Temporal Context-Based Enhanced Momentum Iteration (STCEMI) is proposed for transferability enhancement. First, two spatially and temporally oriented context exploitation strategies are devised, respectively. On the one hand, the blended image is obtained by summing a randomly scrambled version of the original image with itself, and the correction of spatial context momentum to the gradient of the current position is achieved by utilizing the blended image to optimize the perturbation. On the other hand, with the short-time context obtained from single-step iteration along the backward and forward gradient directions, the gradient of the current iteration can be corrected by the temporal context momentum. Second, considering the complementarity of spatial and temporal contexts, two strategies are naturally integrated to construct the spatio-temporal context-based attack, STCEMI, with the objective of achieving stronger transferability. The results of extensive experiments demonstrate that the adversarial images generated by STCEMI achieve the highest cross-model attack success rate across multiple mainstream normally trained and adversarially trained models.