Adversarial AI Attacks – Explained

A full rundown on adversarial AI attacks and how to prevent them

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The advancements in artificial intelligence have played a significant role in many fields. However, these advancements bring new vulnerabilities making AI susceptible to adversarial attacks. This article will cover all you need to know about adversarial attacks on artificial intelligence and how to prevent them.

What Are Adversarial Attacks on AI?

Adversarial AI attacks, also known as adversarial ML (machine learning) attacks, are manipulative actions meant to disrupt a model’s performance. These attacks can result in model malfunctions and inaccurate outputs. 

Types of Adversarial Attacks

Depending on the machine learning algorithm, security violation, and specificity, adversarial attacks can be broadly classified into two main types:

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White Box Attacks. In these types of attacks, the attackers have full knowledge of the inner workings of the AI model, which allows them to build a specific attack for the model.

Black Box Attacks. Unlike the white box attacks, perpetrators of the black box attacks do not have complete knowledge of the internal workings of the AI model. What they simply do is observe the model by monitoring the values of its input and output.

Methods Used in Adversarial Attacks

Several methods are used by perpetrators to carry out adversarial attacks. Some adversarial examples include the following:

  • Evasion Attack
  • Poisoning a chatbot
  • Surrogacy
  • Transferability

Ways to Prevent Adversarial Machine Learning Attacks

Preventing adversarial attacks on ML systems usually involves a complex and time-consuming process. This is because the perpetrators of these attacks usually combine several attack techniques. But with the following defenses, one can still prevent adversarial attacks.

Adversarial Training

This is the most effective way of preventing and stopping a malfunction caused by adversarial attacks. It involves training machines and AI models using adversarial samples. This helps improve the strength of the model to fight against malicious inputs.

Security Updates

Updating the security features of an AI system, such as firewalls and anti-malware programs regularly can help prevent and block adversarial attacks.

Regular Auditing

This involves checking and testing an AI model’s attack detection system regularly for laps and weaknesses.

Data Sanitization

This has to do with checking an AI model for malicious inputs. And once these malicious inputs are detected, they need to be removed instantly. Input validation is used in this method to detect these malicious inputs.