提出新型金融报告攻击方法，可隐蔽地大幅虚增利润
Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack
- 设计多目标攻击策略，自动调整搜索方向以突破反相关约束
- 使公司盈利虚增100%-200%，同时欺诈评分降低15%，成功率超50%
- 结合实务专家意见，确保攻击方式符合真实财务造假场景
不良行为者，主要是陷入困境的企业，有动机和意愿操纵其财务报告以掩盖困境并获取个人利益。作为攻击方，这些企业因可能获得数百万美元收益，且公开可用的财务建模框架众多而具备实施条件。现有攻击方法无法在该数据上生效，因为必须同时满足相互矛盾的目标才能成功。本文提出最大违反多目标（MVMO）攻击，通过自适应调整攻击方向，使满足条件的攻击数量提升20倍。结果表明，在约50%的情况下，企业可将盈利虚增100%-200%，同时将欺诈评分降低15%。我们与律师和专业会计师合作，确保威胁模型真实反映实际财务舞弊行为。
原文摘要 · Abstract (English)
Bad actors, primarily distressed firms, have the incentive and desire to manipulate their financial reports to hide their distress and derive personal gains. As attackers, these firms are motivated by potentially millions of dollars and the availability of many publicly disclosed and used financial modeling frameworks. Existing attack methods do not work on this data due to anti-correlated objectives that must both be satisfied for the attacker to succeed. We introduce Maximum Violated Multi-Objective (MVMO) attacks that adapt the attacker's search direction to find $20\times$ more satisfying attacks compared to standard attacks. The result is that in $\approx50\%$ of cases, a company could inflate their earnings by 100-200%, while simultaneously reducing their fraud scores by 15%. By working with lawyers and professional accountants, we ensure our threat model is realistic to how such frauds are performed in practice.
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