Adversarial Machine Learning: Security Risks and Defense Strategies in AI-Driven Applications

Abstract

As artificial intelligence becomes woven into critical applications such as healthcare, finance, autonomous systems, and cybersecurity, adversarial threats to machine learning models have grown into one of the most pressing concerns in the field. Adversarial machine learning studies how attackers exploit weaknesses in model architectures and data pipelines, manipulating inputs to trigger misclassification, extract sensitive information, or quietly degrade system performance. This article offers

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@article{verma2026adversarial,
  title = {{Adversarial Machine Learning: Security Risks and Defense Strategies in AI-Driven Applications}},
  author = {Harsh Verma},
  year = {2026},
  journal = {International Journal of Scientific Research and Management (IJSRM)},
  doi = {10.18535/ijsrm/v14i02.ec04},
  url = {https://doi.org/10.18535/ijsrm/v14i02.ec04}
}