Adversarial attacks are typically carried out with the intention of causing a malfunction in a machine learning model. This can involve feeding the model inaccurate or misleading data during its training phase or introducing maliciously crafted data to deceive an already trained model.
The main goal of these attacks is to undermine the reliability of the model and compromise the accuracy of its decision-making process. In some cases, adversarial attacks are even aimed at extracting sensitive training data or gaining insights into the inner workings of a model.
As researchers and developers in this field, it is crucial for us to stay vigilant against such threats and continuously enhance our defence against adversarial attacks. By understanding and mitigating these risks, we can ensure that AI and machine learning models remain trustworthy and reliable tools in various domains.