Multiobjective Optimization of Fingerprint Adversarial Examples Generation for Financial Technology Security Based on NSGA-IV
Year: 2026
Type: article
Abstract: The development of financial technology has made significant breakthroughs with the rapid advancement of artificial intelligence (AI) and deep learning models. Fingerprint recognition has long been a commonly used biometric technology and can also be applied to identity verification in the financial sector. However, despite the progress of AI, many security issues still remain. Therefore, it is necessary to ensure the security of financial technology systems throughout their development. This study combines the concept of multiobjective optimization and employs the Nondominated Sorting Genetic Algorithm IV (NSGA-IV) to compute the perturbations required for generating adversarial attacks. Experimental results demonstrate that the proposed method can effectively attack deep learning models, and under the current experimental settings, the attack success rate is highly promising. In addition, the average structural similarity index (SSIM) between the generated samples and the original samples reaches approximately 0.9999. Furthermore, this study conducts ablation experiments on the objective functions to analyze their respective contributions and effectiveness. The experimental results of this method also indicate that current financial technology systems still face many threats from malicious attacks, and thus require effective defense mechanisms to ensure their security and robustness.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,645
OpenAlex ID: W7204752858