Advancements in artificial intelligence have had a significant influence on different fields. This has given quite a number of tech enthusiasts cause for concern. As these technologies expand into different applications, they can result in an increase in adversarial attacks.

What Are Adversarial Attacks in Artificial Intelligence?

Adversarial attacks exploit specifications and vulnerabilities within AI models. They corrupt the data that AI models have learned from and cause these models to generate inaccurate outputs.

Imagine that a prankster changes scrabble tiles arranged as pineapple to become “applepine.” This is similar to what occurs in adversarial attacks.

A few years ago, getting a few incorrect responses or outputs from an AI model was the norm. The reverse is the case now, as inaccuracies have become the exception, with AI users expecting near-perfect results.

When these AI models are applied to real-world scenarios, inaccuracies can be fatal, making adversarial attacks very dangerous. For instance, stickers on traffic signs can confuse an autonomous self-driving car and cause it to move into traffic or directly into an obstacle.

Types of Adversarial Attacks

There are various forms of adversarial attacks. With the increasing integration of AI into everyday applications, these attacks will likely get worse and more complex.

Nonetheless, we can roughly classify adversarial attacks into two types based on how much the threat actor knows about the AI model.

1. White Box Attacks

In white box attacks, threat actors have complete knowledge of the inner workings of the AI model. They know its specifications, training data, processing techniques, and parameters. This knowledge enables them to build an adversarial attack specifically for the model.

The first step in a white box attack is changing the original training data, corrupting it in the littlest way possible. The modified data will still be very similar to the original but significant enough to cause the AI model to give inaccurate results.

That is not all. Following the attack, the threat actor evaluates the model’s effectiveness by feeding it adversarial examples—distorted inputs designed to cause the model to make mistakes—and analyzes the output. The more inaccurate the result, the more successful the attack.

2. Black Box Attacks

Unlike in white box attacks, where the threat actor knows about the AI model’s inner workings, perpetrators of black box attacks have no idea how the model works. They simply observe the model from a blind spot, monitoring its input and output values.

The first step in a black box attack is to select the input target the AI model wants to classify. The threat actor then creates a malicious version of the input by adding carefully crafted noise, perturbations to the data invisible to the human eye but capable of causing the AI model to malfunction.

The malicious version is fed to the model, and the output is observed. The results given by the model help the threat actor to keep modifying the version until they are confident enough that it would misclassify any data fed into it.

Techniques Used in Adversarial Attacks

Malicious entities can use different techniques to carry out adversarial attacks. Here are some of these techniques.

1. Poisoning

Attackers can manipulate (poison) a small portion of an AI model’s input data to compromise its training datasets and accuracy.

There are several forms of poisoning. One of the common ones is called backdoor poisoning, where very little training data is affected. The AI model continues to give highly accurate results until it is “activated” to malfunction upon contact with specific triggers.

2. Evasion

This technique is rather lethal, as it avoids detection by going after the AI’s security system.

Most AI models are equipped with anomaly detection systems. Evasion techniques make use of adversarial examples that go after these systems directly.

This technique can be especially dangerous against clinical systems like autonomous cars or medical diagnostics models. These are fields where inaccuracies can have severe consequences.

3. Transferability

Threat actors using this technique don’t need previous knowledge of the AI model’s parameters. They use adversarial attacks that have been successful in the past against other versions of the model.

For example, if an adversarial attack causes an image classifier model to mistake a turtle for a rifle, the exact attack could cause other image classifier models to make the same error. The other models could have been trained on a different dataset and even have different architecture but could still fall victim to the attack.

4. Surrogacy

Instead of going after the model’s security systems using evasion techniques or previously successful attacks, the threat actor could use a surrogate model.

With this technique, the threat actor creates an identical version of the target model, a surrogate model. The results, parameters, and behaviors of a surrogate must be identical to the original model that has been copied.

The surrogate will now be subjected to various adversarial attacks until one causes it to produce an inaccurate outcome or perform a misclassification. Then, this attack will be used on the original target AI.

How to Stop Adversarial Attacks

Defending against adversarial attacks can be complex and time-consuming as threat actors employ various forms and techniques. However, the following steps can prevent and stop adversarial attacks.

1. Adversarial Training

The most effective step that can prevent adversarial attacks is adversarial training, the training of AI models and machines using adversarial examples. This improves the robustness of the model and allows it to be resilient to the slightest input perturbations.

2. Regular Auditing

It is necessary to regularly check for weaknesses in an AI model’s anomaly detection system. This involves deliberately feeding the model with adversarial examples and monitoring the model’s behavior to the malicious input.

3. Data Sanitization

This method involves checking for malicious inputs being fed into the model. After identifying them, they must be removed immediately.

These data can be identified using input validation, which involves checking the data for patterns or signatures of previously known adversarial examples.

4. Security Updates

It would be difficult to go wrong with security updates and patches. Multi-layered security like firewalls, anti-malware programs, and intrusion detection and prevention systems can help block external interference from threat actors who want to poison an AI model.

Adversarial Attacks Could Be a Worthy Adversary

The concept of adversarial attacks presents a problem for advanced learning and machine learning.

As a result, AI models are to be armed with defenses such as adversarial training, regular auditing, data sanitization, and relevant security updates.