Why Adversarial Machine Learning Is Important for Your Machine Learning Systems

machine learning

The frustration that comes with watching months of your dedicated efforts getting damaged or completely destroyed is hard to comprehend. And when it is done with malicious intent by outside forces, the pain is even more unbearable. Machine Learning developers face a similar situation when they discover there are technologies available for sabotaging ML learning models. Adversarial machine learning is an ML technique designed to trick machine learning models by providing misleading input. They deceive people in some areas, such as spam detection and image classification. 

What Is Adversarial Machine Learning

Adversarial Machine Learning aims to create data instances that can result in the malfunctioning of the Machine Learning model. This is done by suggesting a false prediction or causing a breakdown. These moves often go undetected as they do not raise suspicions. This allows adversarial ML to exploit the numerical representations of ML data.

Machine Learning is on the radar of hackers and disrupters as it increasingly occupies a central position in organizational processes. Organizations cannot leave ML systems vulnerable to attacks. That’s why Adversarial Machine Learning is becoming a crucial aspect of the software industry today.

It is a form of ML involving two diverse adversarial systems. They both need trained separately and combined to generate an output. These outputs can help determine whether there is anything wrong with the system.

Types of Adversarial Machine Learning Attacks

Poisoning Attacks

In this type of attack, it influences the training data to make the model perform less than its potential. It is a form of data contamination achieved by implanting malicious samples. This can impact re-training.

Evasion Attacks

Evasion attacks are widespread and happen during data deployment. The attacker controls the data to mislead earlier classifiers. With these attacks, attackers widely practice in intrusion and malware situations. They come into play during the deployment.

The attackers disguise the content of malware or spam emails to prevent detection. The affected samples are classified as genuine, which affects the data directly affected. 

Model Extraction

Model extraction or stealing means the attacker penetrates a black-box ML system to either reconstruct the model or extract the data it trained on. Such attacks potentially access the stock market prediction model and enjoy financial benefits.

Some of the Commonly Used Adversarial Attack Approaches

FastGradient Sign Method (FGSM)

A simple method exists for creating adversarial examples to minimize the quantum of perturbation added to image pixels. The aim is to generate misclassification. Likewise, LGSM offers efficient computing. But, the downside? Perturbations add on every feature.

Limited-Memory BFGS (L-BFGS)

The L-BFGS or Limited Broyden-Fletcher-Goldfarb-Shanno method is an optimization algorithm that’s non-linear. It is helpful in minimizing the perturbations added to images.

L-BFGS is good for generating adversarial examples. However, as it has box constraints, the method is intensive and time-consuming.

Jacobian-Based Saliency Map Attack (JSMA)

This method employs feature selection to minimize the number of features modified during misclassification. Flat perturbations are added by lowering the salience value. In this type of attack, very few features are perturbed. However, it is more intensive than FGSM.

Deepfool Attack

This is an untargeted adversarial sample generation method for minimizing the distance between perturbed and original samples. This model is effective for creating adversarial examples with minimal perturbations. They are more intensive than other commonly used methods. 

Generative Adversarial Networks GAN

GANs helps generate adversarial attacks in which two neural networks vie with each other. One works as a generator, while the other operates as a discriminator. The two networks perform a zero-sum task. The generator looks to reproduce samples that the discriminator will misclassify. At the same time, the discriminator aims to discriminate between real samples and those created by the generator.

Zeroth-Order Optimization Attack (ZOO)

The ZOO technique helps measure the gradient of classifiers without access to the classifier. It is ideal for black-box attacks. Also, the method estimates gradient and hessian and optimizes perturbations using Adam or Newton’s method. So, the advantage of using the ZOO technique? No need exists for the training of substitute models or data about the classifier.

Carlini & Wagner Attack (C&W)

This technique flows the L-BFGS attack but steers clear of box constraints. It is more efficient at creating adversarial instances and defeating adversarial defenses. 

Addressing Adversarial Vulnerabilities

One proven way to protect against adversarial examples is through adversarial training. In this type of training, machine learning algorithm professionals have to reorient their models on the basis of adversarial examples. The goal is to ensure they are tough and ready to fight data perturbations.

Adversarial training though very effective, is a slow process. It is also expensive. The training examples, each one of them, needs extensively exploring for adversarial weaknesses. The model then needs retrained on all those examples. 

Scientists are working on developing methods that can allow them to discover and patch adversarial weaknesses optimally in machine learning models.

At the same time, AI researchers are also exploring ways of tackling adversarial vulnerabilities in deep learning systems. One method involves uniting parallel neural networks and switching them randomly. This can make the model less vulnerable to adversarial incidents. Another technique consists of creating a comprehensive neural network by combining various other networks. 


Adversarial Machine Learning helps us understand how the model works and learn how it can be tricked. Adversarial ML is an effective way of increasing the stability of the model and understanding unexpected situations and attacks. Through adversarial machine learning, we can also make the models more reliable and comprehensible for the customers. According to experts, adversarial security can develop as a huge area of research and learning in the future. It can become an enormous field within cybersecurity.