Learn how adversarial attacks exploit vulnerabilities in AI systems to achieve malicious goals. Prepare for the IBM Artificial Intelligence Fundamentals certification exam.
Table of Contents
Question
Which of the following describes how adversarial attacks are intentionally carried out on AI systems to accomplish a malicious end goal?
A. Adversarial attacks attempt to exploit AI system vulnerabilities.
B. Adversarial attacks are only carried out by governments against governments.
C. Adversarial attacks attempt to breach the secure facilities holding the computers the AI resides on.
D. Adversarial attacks attempt to clean data sets that the AI is using to train.
Answer
A. Adversarial attacks attempt to exploit AI system vulnerabilities.
Explanation
Adversarial attacks are attempts to exploit vulnerabilities in AI systems to influence the results that are produced by the AI model. In the example used in this course, the adversaries were attempting to make the AI model indicate disease where no disease was present. The primary goal was to make the AI misidentify possible disease in patients.
Adversarial attacks are intentional attempts by malicious actors to exploit weaknesses and vulnerabilities in AI systems in order to manipulate their behavior or outputs to achieve a harmful objective. These attacks do not involve physically breaching secure facilities or corrupting training data.
Some examples of adversarial attacks include:
- Modifying input data in subtle ways to fool an AI system and cause misclassification, such as changing a few pixels in an image to make it incorrectly labeled
- Probing an AI system with specially crafted inputs to uncover information about the model, training data, or sensitive data it was trained on
- Poisoning training data with malicious examples to corrupt the model’s behavior
Adversarial attacks can be carried out by various malicious actors, not just governments. The goal is to undermine the integrity of the AI system by exploiting flaws and blindspots to make it behave in unintended and potentially harmful ways. Defending against adversarial attacks is an important consideration in developing secure and robust AI systems.
In summary, adversarial attacks are malicious exploits that target vulnerabilities in AI systems themselves, rather than physical or data-focused attacks. Understanding how these attacks work is crucial for AI practitioners and those pursuing the IBM AI Fundamentals certification.
IBM Artificial Intelligence Fundamentals certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Artificial Intelligence Fundamentals graded quizzes and final assessments, earn IBM Artificial Intelligence Fundamentals digital credential and badge.