Various methods try to enhance adversarial transferability by improving the generalization from different perspectives. In this paper, we rethink the optimization process and propose a novel sequence optimization concept, which is named Looking From the Future (LFF). LFF makes use of the original optimization process to refine the very first local optimization choice. Adapting the LFF concept to the adversarial attack task, we further propose an LFF attack as well as an MLFF attack with better generalization ability. Furthermore, guiding with the LFF concept, we propose an $LLF^{\mathcal{N}}$ attack which entends the LFF attack to a multi-order attack, further enhancing the transfer attack ability. All our proposed methods can be directly applied to the iteration-based attack methods. We evaluate our proposed method on the ImageNet1k dataset by applying several SOTA adversarial attack methods under four kinds of tasks. Experimental results show that our proposed method can greatly enhance the attack transferability. Ablation experiments are also applied to verify the effectiveness of each component. The source code will be released after this paper is accepted.

提出一种名为LFF的序列优化概念，通过改进原始优化过程达到增强敌对转移性的目的，并应用于敌对攻击任务，进一步提出具有更好泛化能力的LFF攻击和MLFF攻击，以及将LFF攻击扩展为多阶攻击的LLF^N攻击，验证实验结果表明，所提出的方法极大地提升了攻击的转移能力。

从未来的视角看问题：多阶迭代增强对抗攻击的可迁移性