What is Adversarial Transferability

Adversarial Transferability: Understanding and Mitigating Model Vulnerabilities

Artificial intelligence (AI) has revolutionized the way we approach complex problems in various domains. From autonomous vehicles to natural language processing, AI-powered solutions are delivering incredible results that were once unattainable through traditional methods. However, as powerful as these AI models may be, they aren't impervious to malicious attacks, specifically adversarial examples. Adversarial transferability is the study of how adversarial examples can transfer between different AI models and how we can mitigate the vulnerabilities.

What are Adversarial Examples?

Adversarial examples are small, strategically crafted changes to the input of an AI model usually in the form of images, audio or text. They are created to deceive the model and cause it to misclassify the input with a high level of confidence. To an untrained human eye, the adversarial example may appear similar or indistinguishable from the original input. However, the AI model sees it as a new input and processes it differently, leading to incorrect output.

This phenomenon pertains to the fact that the AI model is more sensitive to very small features in the input data, especially in the case of neural networks. This is because neural networks have multiple layers, each of which learns progressively more complex features of the input. As a result, adversarial examples can be created by making small changes to the input based on the learned features of the model's layers. For example, by adding a subtle amount of noise to the pixels of an image or modifying a few specific words in a sentence, the AI model output can be manipulated in a targeted manner.

What is Adversarial Transferability?

Adversarial Transferability is the phenomenon whereby adversarial examples can fool multiple AI models, even if the models are vastly different from each other. In other words, if a particular adversarial example causes one AI model to misclassify an input, there is a high chance that this same adversarial example may also cause a different AI model to misclassify the same input, even if the two models have been trained on different data or use different architectures.

This phenomenon is particularly concerning since it means that building an AI model that is robust against adversarial examples is much harder than previously thought. It is also relevant to the ongoing debate on AI security since it highlights the need to not only train an AI model to perform well under usual conditions but also to ensure that the AI model is not compromised by adversarial examples.

Why is Adversarial Transferability Significant?

The significance of adversarial transferability mostly lies in the potential for malicious actors to attack an AI model using an adversarial example that was not even created with the knowledge of the AI model's details. This means that someone with no knowledge of an AI model's architecture or training data can generate an adversarial example that can fool that model.

In the context of autonomous vehicles or facial recognition systems, an adversarial example could potentially cause critical failures or put people's lives at risk. This is especially concerning since autonomous systems are expected to make decisions in complex, real-world environments that are constantly evolving and unpredictable.

How Can We Mitigate Adversarial Transferability?

The need to mitigate adversarial transferability has led to the development of various defense mechanisms against adversarial attacks. Here are some of the most promising approaches:

1. Adversarial Training:

Adversarial training involves training the AI model with a dataset that includes adversarial examples as well. The idea is that by doing this, the model learns to be more robust against adversarial examples without compromising the model's accuracy on the original data. The downside of this approach is that generating effective adversarial examples requires knowledge of the model's architecture and training data, which may not always be available.

2. Randomizing Inputs:

In this approach, the input data is modified slightly before being fed into the model. This could involve adding random noise to the pixels of an image or modifying some of the words in a sentence. By doing this, the model becomes less sensitive to slight changes in the input data, which makes it harder for adversarial examples to be created. While this approach is effective against simple attacks, it may not be sufficient against more sophisticated attacks.

3. Defensive Distillation:

This approach involves training a second AI model, which is used to approximate and prevent the malicious attacker from learning the first model's mapping from the input to the output. In other words, the second model is used to encrypt the first model's output, making it harder for an attacker to extract information from it. The downside of this approach is that the additional model requires additional computation resources and increases the complexity of the overall system.

4. Adversarial Detection:

This approach focuses on identifying adversarial examples beforehand by training an additional detector model that can distinguish between natural and adversarial data. The idea is that if the detector can spot an adversarial example, the model can disregard the input and return an error message instead of an incorrect classification. While this approach can reduce the damage caused by adversarial examples, it does not fully mitigate the vulnerabilities.


As AI models become more prevalent across different domains, the need to mitigate adversarial attacks increases. Adversarial transferability challenges the assumptions of AI model robustness and highlights the importance of developing more secure models. While there is no one-size-fits-all solution to counter adversarial attacks, we can use a combination of the above techniques to prevent adversarial examples from being successful. As AI continues to thrive, so will the complexity of adversarial attacks. In other words, we need to stay vigilant and continue to innovate to ensure our models remain safe and secure.