Return to Answer

Yes, there is some research on this topic, which can be called adversarial machine learning, which is more an experimental field.

An adversarial example is an input similar to the ones used to train the model, but that leads the model to produce an unexpected outcome. For example, consider an artificial neural network (ANN) trained to distinguish between oranges and apples. You are then given an image of an apple similar to another image used to train the ANN, but that is slightly blurred. Then you pass it to the ANN, which predicts the object to be an orange.

Several machine learning and optimization methods have been used to detect the boundary behaviour of machine learning models, that is, the unexpected behaviour of the model that produces different outcomes given two slightly different inputs (but that correspond to the same object). For example, evolutionary algorithms have been used to develop tests for self-driving cars. See, for example, Automatically testing self-driving cars with search-based procedural content generation (2019) by Alessio Gambi et al.

Yes, there is some research on this topic, which can be called adversarial machine learning, which is more an experimental field.

An adversarial example is an input similar to the ones used to train the model, but that leads the model to produce an unexpected outcome. For example, consider an artificial neural network (ANN) trained to distinguish between oranges and apples. You are then given an image of an apple similar to another image used to train the ANN, but that is slightly blurred. Then you pass it to the ANN, which unexpectedly predicts the object to be an orange.

Several machine learning and optimization methods have been used to detect the boundary behaviour of machine learning models, that is, the unexpected behaviour of the model that produces different outcomes given two slightly different inputs (but that correspond to the same object). For example, evolutionary algorithms have been used to develop tests for self-driving cars. See, for example, Automatically testing self-driving cars with search-based procedural content generation (2019) by Alessio Gambi et al.

Yes, there some research on this topic, which can be called adversarial machine learning, which is more an experimental field.

An adversarial example is an input similar to the ones used to train the model, but that leads the model to produce an unexpected outcome. For example, consider an artificial neural network (ANN) trained to distinguish between oranges and apples. You are then given an image of an apple similar to another image used to train the ANN, but that is slightly blurred. Then you pass it to the ANN, which predicts the object to be an orange.

Several machine learning and optimization methods have been used to detect the boundary behaviour of machine learning models, that is, the unexpected behaviour of the model that produces different outcomes given two slightly different inputs (but that correspond to the same object). For example, evolutionary algorithms have been used to develop tests for self-driving cars. See, for example, Automatically testing self-driving cars with search-based procedural content generation (2019) by Alessio Gambi et al.

Yes, there is some research on this topic, which can be called adversarial machine learning, which is more an experimental field.

An adversarial example is an input similar to the ones used to train the model, but that leads the model to produce an unexpected outcome. For example, consider an artificial neural network (ANN) trained to distinguish between oranges and apples. You are then given an image of an apple similar to another image used to train the ANN, but that is slightly blurred. Then you pass it to the ANN, which predicts the object to be an orange.

Several machine learning and optimization methods have been used to detect the boundary behaviour of machine learning models, that is, the unexpected behaviour of the model that produces different outcomes given two slightly different inputs (but that correspond to the same object). For example, evolutionary algorithms have been used to develop tests for self-driving cars. See, for example, Automatically testing self-driving cars with search-based procedural content generation (2019) by Alessio Gambi et al.

Yes, there some research on this topic, which can be called adversarial machine learning, which is more an experimental field.

An adversarial example is an input similar to the ones used to train the model, but that leads the model to produce an unexpected outcome. For example, consider an artificial neural network (ANN) trained to distinguish between oranges and apples. You are then given an image of an apple similar to another image used to train the ANN, but that is slightly blurred, and you pass it to the ANN, which predicts the object to be an orange.

Several machine learning and optimization methods have been used to detect the boundary behaviour of machine learning models, that is, the unexpected behaviour of the model that produces different outcomes given two slightly different inputs (but that correspond to the same object). For example, evolutionary algorithms have been used to develop tests for self-driving cars. See, for example, Automatically testing self-driving cars with search-based procedural content generation (2019) by Alessio Gambi et al.

Yes, there some research on this topic, which can be called adversarial machine learning, which is more an experimental field.

An adversarial example is an input similar to the ones used to train the model, but that leads the model to produce an unexpected outcome. For example, consider an artificial neural network (ANN) trained to distinguish between oranges and apples. You are then given an image of an apple similar to another image used to train the ANN, but that is slightly blurred. Then you pass it to the ANN, which predicts the object to be an orange.

Several machine learning and optimization methods have been used to detect the boundary behaviour of machine learning models, that is, the unexpected behaviour of the model that produces different outcomes given two slightly different inputs (but that correspond to the same object). For example, evolutionary algorithms have been used to develop tests for self-driving cars. See, for example, Automatically testing self-driving cars with search-based procedural content generation (2019) by Alessio Gambi et al.