Deep learning models are vulnerable to adversarial examples, and adversarial attacks used to generate such examples have attracted considerable research interest. Although existing methods based on the steepest descent have achieved high attack success rates, ill-conditioned problems occasionally reduce their performance. To address this limitation, we utilize the conjugate gradient (CG) method, which is effective for this type of problem, and propose a novel attack algorithm inspired by the CG method, named the Auto Conjugate Gradient (ACG) attack. The results of large-scale evaluation experiments conducted on the latest robust models show that, for most models, ACG was able to find more adversarial examples with fewer iterations than the existing SOTA algorithm Auto-PGD (APGD). We investigated the difference in search performance between ACG and APGD in terms of diversification and intensification, and define a measure called Diversity Index (DI) to quantify the degree of diversity. From the analysis of the diversity using this index, we show that the more diverse search of the proposed method remarkably improves its attack success rate.

翻译：深层次的学习模式很容易受到对抗性的例子的影响,而用来产生这种例子的对抗性攻击引起了相当大的研究兴趣。虽然以最陡峭的下降率为基础的现有方法已经取得了较高的攻击成功率,但附带条件的问题有时会降低其性能。为了解决这一限制,我们使用对此类问题有效的共生梯度(CG)方法,并提出由CG方法(称为Auto Conjudgate Gracient (ACG))攻击所启发的新型攻击算法。在最新强势模型上进行的大规模评价实验的结果显示,对大多数模型来说,ACG能够找到比SOTA算法Auto-PGD(APGD)少迭的更多对抗性例子。 我们调查了ACG和APGD在多样化和强化方面的不同性表现,并界定了一种称为多样性指数(DI)来量化多样性程度的措施。我们从使用这一指数对多样性的分析中可以看出,对拟议方法的更多样化的搜索明显提高了其攻击成功率。