Adversarial Attack

An adversarial attack is a technique used to fool machine learning models by providing deceptive input.

Adversarial attacks are used in various applications such as testing the robustness of models, security research, and understanding model vulnerabilities. They help in identifying weaknesses in models and improving their resilience to malicious inputs.

The process involves creating adversarial examples, which are inputs intentionally designed to cause the model to make a mistake. These examples are often generated by adding small perturbations to legitimate inputs that are imperceptible to humans but cause significant errors in the model’s predictions.

For example, in an image classification task, an Adversarial Attack might involve adding subtle noise to an image of a cat, causing the model to misclassify it as a dog.

Adversarial attacks can be classified into different types based on the attacker’s knowledge of the model, such as white-box attacks (where the attacker has full knowledge of the model) and black-box attacks (where the attacker has no knowledge of the model). Common techniques used in adversarial attacks include gradient-based methods, optimization-based methods, and transferability attacks.

In contrast to data poisoning, the goal of adversarial attacks is not to alter the model in a malicious way, but exploiting the model in its current, imperfect state.

Understanding and defending against adversarial attacks is essential for developing robust and secure machine learning models that can withstand malicious inputs.