Adversarial ML Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack
Автор: CAMLIS
Загружено: 2025-11-13
Просмотров: 30
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Speaker: Edward Raff
Author(s): Edward Raff; Karen Kukla; Michel Benaroch; Joseph Comprix
Abstract: This work explores Adversarial Machine Learning (AML) attacks on financial reporting, demonstrating how bad actors can manipulate financial statements to inflate earnings and reduce fraud scores simultaneously, highlighting a critical information security vulnerability in financial systems.
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