What is Adversarial Testing For Generative AI

With the growing dependency on generative artificial intelligence (AI) platforms, checking their accuracy and authenticity is necessary. Gen AI platforms play a major role across various industries, from writing to creating images and videos or program codes. However, these benefits are accompanied by multiple concerns that need to be addressed to avoid these issues. 

Generative AI

As adversarial testing centers around Gen AI, it is necessary to understand Gen AI first. Generative AI is a type of artificial intelligence model that uses machine learning models to generate content as required. 

It uses pre-existing data to create new data sets. It can generate different types of data, i.e. text, images, videos, or music. The most widely used among them is the ChatGPT which is used for text generation. 

But these generative capabilities are accompanied by certain limitations that are inherent in these language models. These limitations can be kept in check using adversarial testing. 

Adversarial Testing

Adversarial testing is a method for evaluating Gen AI by deliberately providing them with misleading and malicious input. The goal for this is to identify vulnerabilities and weaknesses in the model behavior under confusing circumstances.

This process is also known as “red teaming”. The inputs provided to the model are called “adversarial examples” and are designed to identify weaknesses or unintended outputs made by the Gen AI model. In other words, adversarial testing focuses on recognizing the outputs that are incorrect, biased, or non-relevant to the input.  

Steps For Adversarial Testing

Adversarial testing follows certain steps and they are as follows:

1. The first step is to understand the intended function of the generative AI model. For example, to note if a model assigned for text generation is producing an accurate and comprehensible response to the prompt. 
2. The next step is to create adversarial inputs, i.e. inputs that are slightly distorted versions of the original input. These inputs are created to confuse the AI and to test the output generation in these cases. These inputs can be misleading or contradictory statements meant to confuse the AI models to produce faulty outputs. 
3. After entering the confusing input, the output generated by the model is evaluated and analyzed for irrelevant responses, biased opinions, or harmful information. 
4. The results obtained from the testing are then utilized to improve the model. This process is called fine-tuning and it helps reduce the risk of failures. 

Why is Adversarial Testing Important?

1. Adversarial testing helps ensure that the AI model can handle different types of inputs and generate accurate and relevant results. Minor changes in the input, even for the well-trained AI models, can lead to vast changes in the outputs generated. Thus, adversarial testing is crucial to ensure the robustness of an AI model. 
2. As generative AI models work on machine learning, the output is generated based on the training data set. This can sometimes lead to biased outputs, which can cause harmful consequences in various sectors, especially finance and healthcare. Adversarial testing analyzes the possibility of bias and thus, traines the model further to avoid unfair results. 
3. With the increasing dependency on AI models in day-to-day lives, the potential for misuse of the data also increases. Adversarial testing helps identify the vulnerabilities and potential ethical risks and thus prevent them. 
4. In the cases where the input is not complete due to the user having incomplete information, the output generated can be irrelevant or confusing. Thus, adversarial testing trains the model to generate relevant results under different circumstances and thereby enhances the user experience. 

Challenges in Adversarial Testing

Certain challenges accompany adversarial testing. Some of them are listed below:

1. Designing adversarial inputs for tests is a tedious process, requiring a deep understanding of the model and its vulnerabilities as the Gen AI models are very complex. 
2. With the constant updates in the technology, AI models are also evolving. Thus, there is a need to constantly update the adversarial testing inputs as well, to ensure that the information generated is accurate. 
3. With adversarial testing, it is necessary to be careful as a practice to improve the model might have opposite effects due to the complexity of the models and the vastness of the data.

Conclusion 

Adversarial testing is an important aspect in ensuring the accuracy and reliability of the generative AI models as it simulates potential attacks or vulnerabilities and helps identify their effect on the output generated by the model. With the growing demand and usage of generative AI models, the need for adversarial testing to ensure the effectiveness and safety of these will also keep on increasing.