Adversarial training is the first line of AI defense. It’s a piece of the greater whole that makes up the security model. For more on modular implementation of AI, see my article titled Modular AI. Adversarial training is a machine learning technique designed to make models more robust and secure against malicious manipulation. It involves intentionally generating deceptive inputs (adversarial examples) and including them in the training dataset so the model learns to identify and correctly classify them rather than being fooled.
We’ve all heard the term hallucination being referred to. If you haven’t, a hallucination is where the AI makes a confident statement of truth around a given request. This truth however can be misleading, or just plain fabricated. The AI isn’t lying to you, rather there’s a problem. It could be in the prompt (a vague question), flawed training, incorrect data, etc. Adversarial training comes in during the training process and operates on the training data itself.
How it Works
The adversarial training process is where wrong data is intentionally generated to try and trick the model to accepting it’s accuracy as true. The process itself is a continuous loop or cat and mouse game during the training. The intent is for adversarial training to identify and ignore the noise (bad data) introduced by hackers and focus on the actual, robust features of the data. Here are the steps:
- Step 1 – The Attack – You take normal training data and through specialized algorithms generate an adversarial version. (an incorrect version) For example: We’re training the AI to learn about stop signs. The attacker modifies the pixels slightly and wants to trick the model into believing this is really a speed limit sign.
- Step 2 – The Defense – You label the incorrect or adversarial version correctly. Example: The defense catches the ruse and labels the image as a stop sign (thwarting the attacker who wants it to think it’s a speed sign)
- Step 3 – The Update – You put these examples back into the training data so the model learns not to fall for the trick the next time. Example: By introducing a known attack, defending against it, the model is updated to catch the ruse and continue to process it.
The above is a simple example. The intent is to introduce adversarial or bad data, then defend against it and update the main model accordingly. Here’s an example diagram created for me by Google’s Gemini AI.
Benefits and Tradeoffs
Like most things, there are benefits and tradeoffs. Adversarial training is no different. The benefits of adversarial training is that it makes the model significantly harder to hack. It also forces the model to learn meaningful features instead of superficial patterns. However, the tradeoffs come in the way of a computational expense. This requires more time and processing power. Additionally, this training also results in slightly decreased overall model accuracy. This last part deserves closer attention.
Why Decreased Accuracy?
To understand this, we have to look at how humans see vs. how AI sees.
- Humans look for core, structural features (e.g., a dog has fur, four legs, a snout, and floppy ears).
- AI models look for any mathematical pattern that helps them get the right answer during training.
Many of these patterns are what researchers call non-robust features. These are highly predictive, microscopic patterns in the data that are completely invisible to humans. A normal model relies heavily
on these patterns because they are an easy “shortcut” to getting the right answer. When you perform adversarial training, you are effectively telling the model, “Stop relying on those microscopic patterns, because an attacker can easily manipulate them.” You force the model to ignore
those shortcuts and only use broader, “robust” features (like the actual shape of the object). Because you’ve stripped away a bunch of data patterns the model used to rely on for its standard predictions, its accuracy on normal data drops slightly.
The Solution
The solution to reduced accuracy is to implement modular AI. By placing the adversarial training approach as part of an overall security model would prevent the main model from inheriting the ‘skip the microscopic patterns’. To understand the security model, think of how your virus protection software works. It’s a separate piece of software that protects your system without interfering with the other applications you have installed.
Anti-Virus Architecture for AI
- Adversarial Training – It trains the data before deployment so it inherently recognizes and resists malicious inputs.
- Input Sanitation – The guardrails or firewall. This sits in front of the model in production. It checks incoming user prompts or data for known attack signatures, prompt injections, or malicious noise and blocks them from entering our main model.
- Anomaly Detection – The real time scanner. A secondary model that that monitors the main models internal activation’s or outputs.
- Rate Limiting & API Throttling – The DDoS (distributed denial of service). Attackers often need to to query a model thousands of times to identify the blind spots. Putting speed bumps (rate limiting & API throttling) prevents them from gathering the data they need to build an attack.
Summary
Adversarial training can make a model more robust if implemented correctly. Thankfully industry is moving away from the huge monolithic AI architectural structure and now are implementing a modular approach. This is more cost effective, better for the environment and can easily be updated. It also makes adversarial training more useful as part of the overall security model component. While nothing is 100% certain, this is certainly a good starting point to protect our AI assets.