Attack & Defense in Machine Learning

In machine learning, "attack" refers to methods used to trick or manipulate a model, often by exploiting its weaknesses to produce incorrect outputs. This can involve feeding it misleading data. Conversely, "defense" encompasses strategies to strengthen a model against such attacks, making it more robust and reliable. This might involve improving the model's training process or implementing safeguards to detect and counteract deceptive inputs. Essentially, it's a constant battle to secure the accuracy and integrity of machine learning systems against those who seek to exploit them.