[SPARTA - bodies.dissemination-committee] Submission

Hello, We have submitted a paper on “Understanding Deep Learning defenses Against Adversarial Examples Through Visualizations for Dynamic Risk Assessment” to Neural Computing and Applications journal. If accepted we will acknowledge the SPARTA project. Please see its abstract below. Best wishes, Xabi Title: Understanding Deep Learning defenses Against Adversarial Examples Through Visualizations for Dynamic Risk Assessment Authors: Xabier Echeberria-Barrio, Amaia Gil-Lerchundi, Jon Egaña-Zubia, Raul Orduna-Urrutia Abstract. 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. -- <https://www.vicomtech.org/> Xabier Etxeberria Barrio Researcher | Investigador xetxeberria(a)vicomtech.org +[34] 943 30 92 30 Digital Security | Seguridad digital <https://www.linkedin.com/company/vicomtech> <https://www.youtube.com/user/VICOMTech> <https://twitter.com/@Vicomtech> member of: <https://graphicsvision.ai/> La información que contiene este mensaje y sus adjuntos son confidenciales y están dirigidos exclusivamente a sus destinatarios. Si recibe este mensaje por error, se ruega nos lo comunique y proceda a su borrado. The information contained in this electronic message is intended only for the personal and confidential use of the recipients designated in the original message. If you have received this communication in error, please notify us immediately by replying to the message and deleting it from your computer.