FIMBA: Evaluating the Robustness of AI in Genomics via Feature Importance Adversarial Attacks
This work addresses robustness issues in AI for genomics, which is critical for applications like drug discovery and clinical outcomes, but it is incremental as it builds on existing adversarial attack methods.
The paper demonstrates that AI models used in genomics are vulnerable to adversarial attacks that transform inputs to mimic real data, leading to significant performance deterioration with decreased accuracy and increased false positives and negatives.
With the steady rise of the use of AI in bio-technical applications and the widespread adoption of genomics sequencing, an increasing amount of AI-based algorithms and tools is entering the research and production stage affecting critical decision-making streams like drug discovery and clinical outcomes. This paper demonstrates the vulnerability of AI models often utilized downstream tasks on recognized public genomics datasets. We undermine model robustness by deploying an attack that focuses on input transformation while mimicking the real data and confusing the model decision-making, ultimately yielding a pronounced deterioration in model performance. Further, we enhance our approach by generating poisoned data using a variational autoencoder-based model. Our empirical findings unequivocally demonstrate a decline in model performance, underscored by diminished accuracy and an upswing in false positives and false negatives. Furthermore, we analyze the resulting adversarial samples via spectral analysis yielding conclusions for countermeasures against such attacks.