👉 Against computing, also known as adversarial computing or adversarial machine learning, refers to the practice of developing and deploying systems that are intentionally designed to be vulnerable to manipulation or exploitation by adversaries. This can involve crafting inputs that, while seemingly normal to humans, cause machine learning models to make incorrect predictions or decisions. Adversaries might use subtle perturbations in data, such as adding noise or altering pixel values in images, to deceive models into misclassifying objects or making erroneous outputs. This field highlights the critical need for robustness and security in AI systems, prompting researchers to develop techniques like adversarial training, defensive distillation, and input validation to mitigate these threats.
