7 Types of Adversarial Machine Learning Attacks

What is Adversarial Machine Learning?

Adversarial Machine Learning (AML) attempts to detect, counter, and prevent adversarial attacks that attempt to deceive or exploit machine learning systems.

These attacks may be staged not only through the use of intentionally created inputs, known as adversarial examples but also through data poisoning, which refers to the process of tainting the machine learning model from the training stage.

The seemingly insignificant changes to the input data or the training dataset often go unnoticed by humans, but can drastically alter a machine learning model’s behavior or output, leading to outputs that are misleading or just plain wrong.

The key aspects that make up adversarial machine learning are as follows:

• Adversarial Attacks: These attacks are purposefully designed to deceive machine learning models, often exploiting their vulnerabilities to alter their outputs.

• Data Poisoning: This refers to the manipulation of the model’s training data to adversely impact its learning process, leading to inaccurate or skewed results.

• Defensive Measures: Techniques are developed to protect the models from adversarial attacks and data poisoning, such as adversarial training and defensive distillation.

• Security Evaluations: Regular assessments of the model’s security are crucial to ensure its robustness against adversarial attacks and to improve its defenses.

Now that you’re up to speed with the fundamentals of adversarial machine learning, let’s take a closer look at adversarial attacks and how they’re used to exploit machine learning systems.

Types of Adversarial Machine Learning Attacks

It’s important to understand the various types of adversarial attacks and how they can pose a threat to machine learning systems. These can range from subtle changes in the training phase to an all-out assault on the prediction functionality of the model.

This non-exhaustive list serves to give an overview of some of the most common types of adversarial attacks, allowing a better understanding of the challenges faced.

1. Poisoning Attacks

Poisoning attacks are the earliest attacks that can happen during a machine learning model’s lifecycle.

During the training cycle, malicious data is injected into the training data set which subtly changes the learning process. This causes the model to learn incorrect associations and make false predictions when deployed.

The inaccuracies caused by this tainted data could have serious real-world implications for a machine-learning model. For example, if a model with poisoned training data was used for medical diagnosis it could potentially have serious consequences for patients and businesses alike.

Once a model has been trained on poisoned data, it can be difficult to distinguish whether the model’s poor or unexpected performance is due to data poisoning or other factors such as overfitting, underfitting, or natural noise in the data – making poisoning attacks extremely difficult to identify as a root cause in a finished model.

2. Evasion Attacks

In an evasion attack, input data is intentionally altered in a manner to fool to machine learning model. These subtle alterations are often imperceptible to humans but can cause the model to generate incorrect outputs.

Adversarial examples can be used in this manner to fool the computer vision systems commonly used in self-driving vehicles. An attacker could subtly manipulate the image of a stop sign by adding a few pixel-level changes that are unnoticeable to a human, but enough to cause the model to misclassify the sign as something else.

Identifying evasion attacks relies heavily on an understanding of the model’s existing behavior combined with robust evaluation techniques. A common proactive solution for evasion attacks is to intentionally use adversarial examples in the model training – making the model more robust in the case of an adversarial evasion attack.

3. Membership Inference Attacks

Membership inference attacks aim to determine if specific information was used within the training data set of machine learning models.

This can be used to infer sensitive information about individuals whose data might have been included in the training set which poses an obvious security and privacy threat.

For instance, if a machine learning model was developed by an institution to determine the credit score of their customers, an attacker may infer that the person’s financial history was included in the training set. This could disclose sensitive information about the individual’s financial status and credit situation.

Like previous examples, membership inference attacks are hard to identify. Potential solutions involve modifying the models themselves to prevent how much information they can reveal about a single data point.

4. Model Inversion Attacks

Unlike membership inference attacks -which aim to identify if certain data was used in training – model inversion attacks attempt to extract information directly from the model’s training data.

By observing the model’s output, an attacker can make educated guesses about the data it was trained on, potentially revealing private or sensitive information.

Consider a machine learning model used for facial recognition. If an attacker had access to this model, but not the training data, they could cleverly manipulate the model to match a targeted individual’s facial features, effectively inverting the model.

Model inversion attacks can be challenging to detect as they are essentially a brute-force approach. Simple fixes might be to limit the number of queries that can be made in a given timeframe or the addition of noise or randomness to the outputs.

5. TrojAI Attacks

TrojAI attacks occur when an attacker embeds a hidden trigger within the machine learning model during its training phase.

The model will behave normally until it encounters specific input related to the trigger, upon which it provides incorrect outputs in favor of the attacker. TrojAI attacks are particularly deceptive as they are not seen under normal operating conditions.

An attacker might subtly modify the model of a city’s traffic controls so that it operates normally most of the time. Under certain rare conditions, the attack is triggered causing the model to manipulate the traffic signals leading to chaos and potentially dangerous situations.

Detecting TrojAI attacks is challenging due to their covert nature. They are invisible under normal circumstances and only appear under very specific, predefined conditions. A potential solution could be the application of rigorous testing protocols with a wide array of diverse scenarios to uncover hidden triggers.

6. Model Extraction Attacks

Model extraction attacks are in essence intellectual property thefts of machine learning algorithms.

Also known as model stealing attacks, the goal is to create a duplicate of the target machine learning model without actually having access to the training data set.

Consider a machine learning system that has been in development by a large tech company. After years of research and development, an attacker could leverage model extraction to effectively clone the model.

To do this, the attacker starts by querying the model with a range of inputs. He’ll then collect the outputs and use this information to train a new model using a fraction of the time or costs involved.

Detecting model extraction attacks can be particularly challenging as there is little difference in the use of the tools when compared to normal use. Some indicators would include the frequency of inputs or strange ranges of inputs.

To mitigate such attacks, solutions like rate limiting the API queries and adding random noise to the responses could help to hinder the efforts of a would-be attacker.

7. Byzantine Attacks

Byzantine attacks involve a bad actor that sends misleading or conflicting information to different parts of a distributed system with the intention of causing the system to fail or behave unexpectedly.

In distributed machine learning systems, where various individual models collaborate to reach a collective decision or prediction, some models might start sending manipulated or inconsistent data to others, causing erroneous outcomes.

Detection of Byzantine attacks is notably challenging due to their decentralized nature – they occur in complex, distributed systems where individual components operate semi-independently and there might not be a single point of control or observation.

It’s often hard to tell if an inconsistency is due to a Byzantine attack, a regular failure, or simply the result of the system’s inherent complexity.

Defenses against Byzantine attacks often involve building redundancy and robustness into the system. Techniques such as Byzantine fault tolerance algorithms can be used, which involve having multiple redundant components and using majority voting or other forms of consensus to arrive at decisions.

Examples of Real-World Adversarial Attacks

Adversarial machine learning attacks are more than just theory at this point and there have been a few examples of them being used in the real world.

• Gmail Spam Filtering: Researchers demonstrated attacks that could bypass Gmail’s spam filters by making subtle modifications to specific words and phrases in their emails. For example, changing “online pharmacy” to “onIine pharmac_y” could bypass the filter.
• Tesla Autopilot: Researchers fooled Tesla’s image recognition system for Autopilot by placing small stickers on stop signs to make them invisible to the AI system effectively making the car able to run through the stopping points.
• Face recognition: In 2019, researchers showed they could create 3D-printed glasses to fool facial recognition systems into thinking someone was a different person.
• DeepNude: This controversial deepfake app used AI to digitally remove clothing from images of women. It was designed to evade detection systems and spread nonconsensual fake nude images.
• DeepLocker: An attack demoed by IBM in 2018 that could hide malware inside a video that only unveiled it when recognizing a specific person’s face using AI.
• CAPTCHA solving: Spammers have developed machine learning systems to solve CAPTCHAs automatically, enabling them to create fake accounts at scale to spread malicious content or ads.

While the list is far from exhaustive, these are a few examples of how adversarial machine learning can be deployed on real-world systems.

Case Study: Exploiting ChatGPT and AI Chatbots

Researchers at Carnegie Mellon University uncovered a significant vulnerability in several advanced AI chatbots, including ChatGPT, Google’s Bard, and Claude from Anthropic.

These chatbots, developed to avoid producing undesirable content, were found susceptible to a simple manipulation technique.

The study aimed to explore the resilience of AI chatbots against adversarial attacks, particularly examining how simple text manipulations could bypass safeguards designed to prevent the output of harmful or undesirable content.

The experiment revealed that all tested AI chatbots could be manipulated into breaking their operational constraints.

Despite the companies’ efforts in deploying countermeasures upon being notified, the researchers noted that the fundamental issue remained unaddressed, with thousands of potential manipulative strings identified that could still exploit the chatbots.

The case of adversarial text manipulations against AI chatbots like ChatGPT, Bard, and Claude reveals a pressing issue in the field of artificial intelligence concerning security and reliability.

As these models become increasingly integrated into societal functions, ensuring their robustness against adversarial attacks becomes paramount. This case study spotlights the complexity of AI security, the imperative for continuous research and development in model hardening, and the broader implications for AI’s role in critical decision-making processes.

Adversarial Machine Learning at Scale

As the reach of machine learning models expands, the scale of their deployment has become somewhat of a playground for adversarial machine learning. Understanding the potential for adversarial machine learning at scale is vital to safeguard many modern systems as they expand, and integrate machine learning models.

As machine learning systems grow and expand, so does the attack surface they offer to would-be wrong wrongdoers. An attacker can infuse adversarial examples into the model’s training data, subtly changing the model’s learning process. At scale, the implications of these manipulations can be potentially catastrophic.

Just picture a fleet of self-driving cars or a vast network of facial recognition systems. Once compromised by an adversarial attack, the consequences can rapidly become widespread, with the potential to impact thousands of people.

In essence, the larger the scale of deployment, the higher the stakes. A single adversarial example in the training dataset can have massive implications when the model is fully deployed to a population. Ensuring the security of these models is paramount, and adversarial machine learning at scale should be a top consideration in the design and deployment of systems.

Adversarial Attack Defense Mechanisms

So far we’ve briefly touched on defense mechanisms for adversarial machine learning models. While no single ‘silver bullet’ defense exists, many of these techniques can be combined to increase their effectiveness against adversarial attacks.

• Adversarial Training: This method involves incorporating adversarial examples into the training data. The idea is that by exposing the model to an example adversarial attack during training, it will learn to correctly classify such examples when encountered in the future.

• Defensive Distillation: In this approach, the model is trained to output probabilities of different classes, instead of hard decisions. The second round of training is then performed on these soft labels. This process tends to make the model’s decision boundaries smoother, making it more difficult for an attacker to create adversarial examples that cross these boundaries.

• Gradient Obfuscation: This technique involves modifying the machine learning model to hide the gradients, making it harder for an adversary to generate adversarial examples using gradient-based methods.

• Feature Squeezing: This defense mechanism involves reducing the search space available to an adversary by squeezing unnecessary details from the inputs while preserving the critical features.

• Regularization: Regularization methods such as L1 and L2 can be used to prevent the overfitting of the model to adversarial examples.

• Randomization: Randomization introduces randomness in the model’s architecture at inference time to make it harder for an adversary to craft successful adversarial examples.

• Input Transformations: Techniques like image quilting, JPEG compression, and total variance minimization can help to remove the adversarial perturbations from the input data.

• Detector Networks: These are auxiliary networks trained to detect adversarial examples.

• Self-supervised Learning: Using self-supervised learning approaches to improve the model’s understanding of the input data, making it more robust against adversarial attacks.

• Ensemble Methods: Ensemble models, which aggregate the predictions of multiple base models, can increase the diversity of decision boundaries, making it harder for an adversary to find a single adversarial example that fools all base models.

The Future of Adversarial Machine Learning

The future of adversarial machine learning will be characterized by the ongoing battle between attack strategies and available defensive mechanisms.

The increased integration of deep learning models and neural networks into existing digital systems should emphasize the need to be continuously adapting and evolving in an attempt to stay one step ahead of adversarial attacks.

One foreseeable trend is the rise of more complex yet subtle adversarial examples. As defenses improve, so will the craftiness of the attacks. We can expect to see adversarial attacks that not only fool models but do so in a way that is harder to detect and defend against. This can involve more refined manipulation of the input data or more insidious methods of data poisoning.

Defenses will likely focus on enhancing the robustness of machine learning algorithms. Adversarial training will become increasingly important, using more sophisticated adversarial examples. We might also hope to see the development of new types of neural networks specifically designed to be resilient to adversarial manipulation.

Like everything in AI, the rate of change when it comes to adversarial machine learning is difficult to keep up with, let alone predict where it might be a year from now.

That’s all from me, for now, 🙂 -Matt.