Adversarial Machine Learning: Meaning, examples & how it works

Adversarial machine learning is a branch of machine learning that focuses on the vulnerabilities of machine learning models to various attacks.

An adversarial input is any machine learning input that aims to fool the model into making the wrong predictions or producing wrong outputs.

Because adversarial attacks can have serious consequences, including in the security, fraud, and healthcare sectors, researchers focus on discovering different attack methods, as well as developing defense mechanisms against them.

This post explores the adversarial machine learning world and includes examples, challenges, and ways to attack and defend AI models.

What Is Adversarial Machine Learning?

Adversarial machine learning studies a class of attacks that are aimed at reducing the performance of classifiers on specific tasks. In other words, they aim to fool the AI machine.

As the use of artificial intelligence and machine learning techniques becomes more widespread, the risk of adversarial attacks increases. This presents a significant threat to various AI-powered applications, including spam detection, personal assistants, computer vision, and so on.

How Adversarial Attacks Work

An adversarial attack is any process designed to fool a machine learning model into causing mispredictions. This can happen during training, as well as in a live execution environment. In other words, if you can figure out a way to fool or sabotage the model, then you have successfully attacked it.

What Is An Adversarial Example?

An adversarial example is any specially designed input for a machine learning model that aims to cause the model to make a mistake or produce an incorrect output.

You can create an adversarial example by making slight changes to the input data, which though might not be visible to the human eye, is often enough to change the model’s understanding and lead it to make erroneous outputs.

Adversarial examples are used in the training stages of an AI model and the modifications made are typically generated using various optimization techniques, including gradient-based methods like the Fast Gradient Sign Method (FGSM) Attack, which exploits the sensitivity of the model to changes in the input space.

The goal with adversarial examples is to add slight perturbations to the input data that might be barely visible to human observers, but are still significant enough to lead the model into misclassifying the input.

Adversarial attacks can happen in different machine learning sectors, including image recognition and natural language processing.

Applications of Adversarial ML

The ability to detect and exploit weaknesses in any artificial intelligence platform has a wide range of uses, as the attacker is only limited by his imagination. Here are some of the many ways that a hacker can leverage a compromised AI machine using adversarial machine learning methods.

Image & Video Recognition: From content moderation to autonomous vehicles and surveillance systems, a lot of artificial intelligence applications rely on image and video recognition algorithms. By altering the machine’s input and compelling it to misclassify stuff, an attacker can evade whatever control systems rely on its object recognition capabilities. For autonomous vehicles, such a manipulation can lead to road accidents.
Spam Filtering: Spammers can successfully bypass AI spam detection systems by optimizing their spam emails with different structures, more good words, fewer bad words, and so on.
Malware Detection: It is equally possible to craft malicious computer code that can evade detection by malware scanners.
Natural Language Processing: By misclassifying text using adversarial machine learning, the attacker can manipulate text-based recommendation systems, fake news detectors, sentiment detectors, and so on.
Healthcare: Attackers can manipulate medical records to either alter a patient’s diagnosis or deceive the system into revealing sensitive medical records.
Financial Fraud Detection: AI systems employed in financial fraud detection are also at risk from adversarial machine learning attacks. For instance, an attacker can create synthetic data that mimics legitimate transactions, thereby, making it possible to conduct fraud undetected by the model.
Biometric Security Systems: By employing manipulated data, an attacker can beat fingerprint or facial detection security systems to gain unauthorized access to a network or platform.
Adversarial Defense: While most of the foregoing uses are for attacking a system, adversarial defense is the study of adversarial attacks for use in creating robust defense systems against attackers of the machine.

Consequences Of Adversarial ML

Adversarial machine learning has consequences that can impact the reliability or performance of AI systems. Here are the major ones.

Erodes Trust: If adversarial attacks should grow and get out of hand, it will cause the erosion of trust for AI systems, since the public will come to view any machine-learning based system with a level of suspicion.
Ethical Implications: The application of machine learning systems to domains such as healthcare and criminal justice raises ethical questions, as any compromised AI system can cause severe personal and social damage.
Economic Implications: Adversarial attacks can lead to financial loss, increased security costs, financial market manipulation, and even reputation damage.
Increased Complexity: The threat of adversarial attacks increases the research effort and overall complexity of machine learning systems.
Model Theft: An AI model itself can be attacked to probe for and retrieve internal parameters or information about its architecture that can be employed for a more serious attack on the system.

Types of Adversarial Attacks

There are different types of adversarial machine learning attacks, and they vary depending on the attacker’s goals and how much access he has to the system. Here are the major types.

Evasion Attacks: In evasion attacks, adversaries modify inputs to trick the AI system into misclassifying them. This can involve adding imperceptible perturbations (or deliberate noise), to input images or other data to deceive the model.
Data Poisoning Attacks: Data poisoning attacks occur during the training phase of an AI system. By adding bad (or poisoned) data into the machine’s training dataset, the model becomes less accurate in its predictions and is, therefore, compromised.
Model Extraction Attacks: In model inversion attacks, adversaries exploit the ability to extract sensitive information from a trained AI model. By manipulating inputs and observing the model’s responses, they can reconstruct private data, such as images or text.
Transfer Attacks: This refers to the ability of an attack against one machine learning system to be equally effective against another machine learning system.

How To Defend Against Adversarial Attacks

There are different defense mechanisms that you can use to protect your AI model against adversarial attacks. Here are some of the most popular ones.

Creating Robust Systems: This involves the development of AI models that are more resistant to adversarial attacks by including tests and evaluation guidelines to help the developers identify system flaws that might lead to adversarial attacks. They can then develop defenses against such attacks.
Input Validation: Another approach is to check the inputs to an ML model for already known vulnerabilities. The model could be designed to reject inputs, for example, that contain modifications known to cause machines to make wrong predictions.
Adversarial Training: You could also introduce some amount of adversarial examples into your system’s training data to help the model learn to detect and reject adversarial examples in the future.
Explainable AI: Theoretically, the better developers and users understand how an AI model functions deep down, the easier it will be for people to come up with defenses against attacks. Therefore, an explainable AI (XAI) approach to machine learning and AI model development can solve a lot of problems.

Conclusion

Adversarial machine learning attacks pose a significant threat to the reliability and performance of artificial intelligence systems. However, by understanding the different types of well-known attacks and implementing defense strategies to prevent them, developers can better protect their AI models from adversarial attacks.

Finally, you should understand that the fields of AI and adversarial machine learning are still growing. So, there may still be other adversarial attack methods out there that are yet to become public knowledge.