A Deep Dive into Adversarial Machine Learning

Can you imagine something or someone hacking into your Machine Learning model?

It will impact various technologies like face recognition or license plate recognition that are reliant on ML algorithms. Moreover, digital attacks taking place now and then pose a great threat to our data.

One such threat is adversarial machine learning.

Humanity is progressing towards a digital age, and the need to protect computing systems with sensitive data is increasing. With rising threats of digital attacks, cybersecurity is becoming more prominent every day.

Adversarial machine learning is one such digital attack that fools machine learning models with deceptive data, resulting in incorrect decisions.

What is Adversarial Machine Learning?

Adversarial machine learning is a machine learning technique that uses deceptive inputs to trick advanced machine learning models into providing erroneous output.

Adversarial machine learning methods generate and detect  deceptive inputs that deceive classifiers.

Researchers have been exploring this over the past few decades. They have found that it poses a great threat to image classification and spam detection technologies. When used in image recognition, adversarial machine learning modified the images to produce inaccurate decisions.

But how can someone get an adversarial example?

An adversarial attack generates adversarial examples, which are deceptive inputs that influence machine learning models to produce incorrect predictions.

An adversarial example appears as a valid input to humans but can cause an advanced ML model to be inaccurate.

There are mainly two types of adversarial attacks, namely white-box, and black-box attacks.

In white-box attacks, the attacker has access to the model’s architecture and its parameters, whereas, in black-box attacks, they get to observe only the output of the target model.

Adversarial Attacks in Machine Learning

Every organization is integrating machine learning in one way or the other to their core to attract investors or clients. However, increasing dependency on ML algorithms makes organizations vulnerable to digital attacks, and hence the need to protect them is increasing.

Thus, adversarial machine learning is becoming a more prominent area of research every passing day.

Tech giants like Google and Microsoft have started investing in future-proof security systems as they mostly run on Machine Learning algorithms.

Moreover, securing machine learning systems is the need of the hour because companies like Google, Amazon, and Tesla are facing some adversarial attacks. We will have a future where we would have to trust the machines, so eradicating all the threats that influence its decision is important for humanity.

Many governments are deploying checklists that test the trustworthiness of some AI and machine learning systems.

Photo by Jefferson Santos on Unsplash

Recent studies show that privacy is a big concern for these cloud-based machine learning models. Even though the focus is still on traditional security, we are working towards creating production-grade AI systems with advanced security to prevent adversarial machine learning attacks.

Companies like Tooliqa use deep learning methods to create sophisticated tools to improve accessibility and human experiences.

They are also investing in cybersecurity, and digital attacks like adversarial machine learning threaten the trustworthiness of their tools.

Startups like MegVii are building world class deep learning algorithms that counter adversarial examples and Verkada are focused on building security systems to counter any digital attacks.

How do Adversarial Attacks work in AI Systems?

Adversarial attacks are of many types and can be dangerous for advanced machine learning models also. Most of these attacks target deep learning systems. Some even target traditional machine learning models like Support Vector Machines (SVMs) or linear regression.

The common purpose of these attacks is to fool the machine learning algorithm to produce erroneous output and thus deteriorate the performance of classifiers.

Adversarial machine learning as a field tries to study the attacks that deteriorate the performance of classifiers in particular tasks.

Broadly, there are three types of adversarial attacks:

  • Poisoning Attacks

This attack refers to altering or contaminating the training data or labels to underperform the ML model when deployed. The attacker performs adversarial contamination to the training data, hence the name poisoning attack.

While a programmer can retrain ML systems by collecting data during operation, attackers try to inject malicious samples to disrupt the retraining of a model.
  • Evasion Attacks

One of the most ubiquitous and most researched types of attacks involves the attacker manipulating the data during deployment. This is done with the intent to deceive classifiers that programmers have trained previously. Being performed in the deployment phase, its practical and immensely dangerous in intrusion and malware scenarios.

The attacker’s main aim here is to evade detection by concealing the malware content by making it appear as a legitimate input.

This does not impact the training data.

  • Model Extraction

It’s a black box machine learning attack that tries to extract or steal the model to either rebuild them or extract the data upon which they were trained. It’s useful in situations involving a model or training data that is confidential or sensitive.

Adversaries or attackers use model extraction for their benefit, like, for instance, stealing a stock market prediction for their benefit.

Photo by Jake Walker on Unsplash

How to Make Machine Learning Algorithms More Trustworthy

The type of adversarial attacks we’ve come across so far has established the fact that Machine Learning can break your trust or simply “can be fooled.” So, we need to defend the ML algorithms against such digital attacks by making them capable of automatically detecting adversarial attacks.

There are three steps to do that:

  • Using Denoising Ensembles

Many images are initially equipped with added noise to deceive the classifier. Denoising ensembles aim to remove any previously added noise that may try to deceive the defender.

We need to develop a denoiser that extracts the original uncorrupted images by removing most of the noise from a corrupted image to improve image classification accuracy.

  • Using Verification Ensembles

The denoised images by different denoisers obtained in the first step are verified by a group of classifiers. Each classifier targets a different denoised image and classifies it.

Then, the verification ensemble votes to determine the final category to which the image may belong.

Since some images may not have been denoised completely so, voting helps us in making a more accurate classification.

  • By Increasing Diversity

It’s very important to make our Machine Learning models diverse. A diversified group of denoiser can be used to denoise a variety of images that may have been corrupted by creating various images with different noise levels. A diversified group of verifiers will aid in generating a variety of classifications, making it difficult for adversarial attackers to corrupt images to deceive ML models.

In this new digital age, we need to protect ourselves from digital attacks.

When we deploy a machine learning model, we must look out for adversarial attacks instead of blindly trusting the decisions of the model.

We have covered all the types of adversarial machine learning attacks that can be harmful and mentioned a few steps to strengthen our defense against adversarial examples.

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