Understanding Deep Learning defenses Against Adversarial Examples Through Visualizations for Dynamic Risk Assessment
<|reference_start|>Understanding Deep Learning defenses Against Adversarial Examples Through Visualizations for Dynamic Risk Assessment: In recent years, Deep Neural Network models have been developed in different fields, where they have brought many advances. However, they have also started to be used in tasks where risk is critical. A misdiagnosis of these models can lead to serious accidents or even death. This concern has led to an interest among researchers to study possible attacks on these models, discovering a long list of vulnerabilities, from which every model should be defended. The adversarial example attack is a widely known attack among researchers, who have developed several defenses to avoid such a threat. However, these defenses are as opaque as a deep neural network model, how they work is still unknown. This is why visualizing how they change the behavior of the target model is interesting in order to understand more precisely how the performance of the defended model is being modified. For this work, some defenses, against adversarial example attack, have been selected in order to visualize the behavior modification of each of them in the defended model. Adversarial training, dimensionality reduction and prediction similarity were the selected defenses, which have been developed using a model composed by convolution neural network layers and dense neural network layers. In each defense, the behavior of the original model has been compared with the behavior of the defended model, representing the target model by a graph in a visualization.<|reference_end|>
@article{echeberria-barrio2024understanding,
    title={Understanding Deep Learning defenses Against Adversarial Examples
  Through Visualizations for Dynamic Risk Assessment},
    author={Xabier Echeberria-Barrio, Amaia Gil-Lerchundi, Jon Egana-Zubia, Raul
  Orduna-Urrutia},
    journal={Neural Comput and Applic 34, 20477 to 20490, 2022},
    year={2024},
    doi={10.1007/s00521-021-06812-y},
    archivePrefix={arXiv},
    eprint={2402.07496},
    primaryClass={cs.LG cs.CR}
}