Traditional AI Models Protection

Artificial intelligence (AI) is rapidly transforming our world, from facial recognition software authenticating your phone to spam filters safeguarding your inbox. But what if these powerful tools could be tricked? Adversarial attacks are a growing concern in AI security, where attackers manipulate data to cause AI systems to make critical mistakes. Gartner predicts that 30% of cyberattacks will target vulnerabilities in AI, either through manipulating training data, stealing the AI model entirely, or tricking it with deceptive inputs, highlighting the urgency of addressing these vulnerabilities.

Traditional AI models can be surprisingly susceptible to these attacks. Imagine a self-driving car mistaking a stop sign for a yield sign due to a cleverly placed sticker. A 2018 study by researchers, found that adding just a few strategically placed stickers on traffic signs could trick a deep learning model into misclassifying the sign with a staggering 84% success rate*. The consequences of such an attack could be catastrophic. But how exactly do these attacks work?

Adversarial attacks come in many forms, all aiming to manipulate an AI model’s decision-making processes. Here are some common techniques that attackers use to exploit models:

Adding imperceptible noise:

Imagine adding minuscule changes to an image, invisible to the human eye, that completely alter how an AI classifies it. For instance, adding specific noise to a picture of a cat might trick a facial recognition system into identifying it as a dog.

Crafting adversarial inputs: 

Attackers can create entirely new data points that an AI model has never seen before. These examples are specifically designed to exploit the model’s weaknesses and force it to make a wrong prediction.

Poisoning:

In some cases, attackers might try to manipulate the training data itself. By injecting perturbations into the data used to train an AI model, they can influence the model’s behavior from the ground up.

Extraction:

Attackers can try to steal or replicate the underlying model by querying it extensively and analyzing the responses. This attack tries to reverse-engineer the AI model, effectively “stealing” its intellectual property, leading to intellectual property theft.

Inference:

In some cases, attackers try to extract sensitive information from the model’s output. They try to analyze the model’s response to various inputs; attackers can infer confidential data, such as personal user information or proprietary data used in the training model.

The susceptibility of AI models to adversarial attacks varies depending on their architecture. Even models with millions of parameters can be fooled with cleverly crafted attacks.

Mitigating attacks with Styrk

Enterprise usage of AI is increasingly threatened by adversarial attacks, where AI models are deceived using manipulated data. To address this, Styrk offers its AI security product, Armor,  which assesses and enhances the robustness of AI models. Armor scans labeled data and performs pre-selected adversarial attacks on it. After executing these attacks, the system identifies any vulnerabilities and reports them to the customer in a comprehensive report format. 

In addition to identifying adversarial attacks, Styrk’s Armor also proposes defense mechanisms against adversarial attacks. As attacks continue to increase and evolve constantly, Armor keeps adding new attacks and defenses to its systems, keeping ahead of the curve in developing robust solutions that customers can use to keep their AI models safe and performant. At Styrk, we provide solutions that can help identify such attacks and propose mitigation mechanisms to ensure that AI technology helps, not hinders, enterprises. 

Contact us to understand how Armor can help safeguard your AI model from adversarial attacks. 

*https://openaccess.thecvf.com/content_cvpr_2018/papers/Eykholt_Robust_Physical-World_Attacks_CVPR_2018_paper.pdf