一句话：提出 CoEvA2，用多目标搜索生成满足领域约束的对抗样本，并通过对抗训练提升信用评分系统鲁棒性。
信用评分是关键的金融科技应用，现有基于机器学习的决策系统易受对抗攻击。然而，传统对抗攻击生成的样本常违反领域约束，成为实际不可行的假阳性。为此，作者提出 CoEvA2：一种基于搜索的受约束对抗样本生成方法，采用多目标搜索同时满足领域约束、实施攻击并最大化请求的透支金额。作者在大型银行真实信用评分系统上评估：CoEvA2 能生成数千个有效对抗样本，揭示银行系统高风险；进一步利用这些样本进行对抗训练后，系统鲁棒性提升，使攻击失败。
Credit scoring systems are critical FinTech applications that concern the analysis of the creditworthiness of a person or organization. While decisions were previously based on human expertise, they are now increasingly relying on data analysis and machine learning. In this paper, we assess the ability of state-of-the-art adversarial machine learning to craft attacks on a real-world credit scoring system. Interestingly, we find that, while these techniques can generate large numbers of adversarial data, these are practically useless as they all violate domain-specific constraints. In other words, the generated examples are all false positives as they cannot occur in practice. To circumvent this limitation, we propose CoEvA2, a search-based method that generates valid adversarial examples (satisfying the domain constraints). CoEvA2 utilizes multi-objective search in order to simultaneously handle constraints, perform the attack and maximize the overdraft amount requested. We evaluate CoEvA2 on a major bank's real-world system by checking its ability to craft valid attacks. CoEvA2 generates thousands of valid adversarial examples, revealing a high risk for the banking system. Fortunately, by improving the system through adversarial training (based on the produced examples), we increase its robustness and make our attack fail.
@inproceedings{paperbot5815,
title = {Search-based adversarial testing and improvement of constrained credit scoring systems},
author = {Salah Ghamizi and Maxime Cordy and Martin Gubri and Mike Papadakis and Andrey Boystov and Yves Le Traon and Anne Goujon},
booktitle = {FSE},
year = {2020},
doi = {10.1145/3368089.3409739}
}