Almost anyone can contaminate a machine learning (ML) dataset, substantially and permanently altering its behavior and output. With careful and proactive detection efforts, organizations can sustain weeks, months, and even years of work to repair damage caused by contaminated data sources.
What is data addiction and why is it important?
Data poisoning is a type of adversarial ML attack that maliciously tampers with datasets to mislead or confuse models. The goal is to make it react incorrectly or behave in an unintended way. In reality, these threats could be detrimental to the future of AI.
As AI adoption expands, data addiction is becoming more common. Intentional manipulation increased the frequency of model hallucinations, inappropriate responses, and misclassifications. Public trust is already declining. Only 34% of people strongly believe they can trust technology companies with AI governance.
Examples of Machine Learning Dataset Addiction
Although different types of addiction exist, they all share the goal of influencing the output of an ML model. Typically, each step involves providing inaccurate or misleading information in an attempt to change behavior. For example, someone could insert an image of a speed limit sign into a stop sign dataset to trick a self-driving car into misclassifying a road sign.
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Even if an attacker does not have access to the training data, they can leverage the ability to adjust the model's behavior to disrupt the model. Thousands of targeted messages can be entered at once, skewing the classification process. Google experienced this situation a few years ago when attackers ran millions of emails simultaneously to confuse email filters into misclassifying spam as legitimate correspondence.
In another real-world case, user input permanently changed the ML algorithm. In 2016, Microsoft launched a new chatbot called 'Tay' on Twitter that imitated the conversational style of a teenage girl. In just 16 hours, over 95,000 tweets were posted. Most of them were hateful, discriminatory or offensive. Companies quickly discovered that people were submitting inappropriate inputs in large quantities to change the model's results.
Common Dataset Poisoning Techniques
Addiction techniques can be classified into three general categories: The first is data set tampering. This is when someone maliciously changes the training material, affecting model performance. A typical example is an injection attack, where an attacker inserts inaccurate, offensive, or misleading data.
Label flipping is another example of tampering. In this attack, the attacker simply switches training materials to confuse the model. The goal is to misclassify or significantly miscalculate, which in turn significantly changes performance.
The second category includes model manipulation during and after training, where an attacker incrementally modifies the algorithm to influence it. An example is a backdoor attack. In this case, someone pollutes a small subset of the data set. After launch, we will flag specific triggers that cause unintended behavior.
The third category includes manipulating the model after deployment. One example is split view addiction, where someone takes control of the sources indexed by an algorithm and fills them with inaccurate information. When an ML model uses newly modified resources, it adopts tainted data.
The importance of proactive detection efforts
When it comes to data contamination, being proactive is important to predict the integrity of your ML model. While the unintended behavior of chatbots can be offensive or derogatory, poisoned cybersecurity-related ML applications have much more serious implications.
If someone gains access to your ML dataset and contaminates it, your security can be seriously compromised. For example, classification errors may occur during threat detection or spam filtering. Tampering typically occurs gradually, so no one will discover the attacker's presence for an average of 280 days. In order not to overlook this, companies must take active steps.
Unfortunately, malicious tampering is very simple. In 2022, a research team found that it was possible to pollute 0.01% of the largest datasets, COYO-700M or LAION-400M, for just $60.
This small percentage may not seem like much, but even small amounts can have serious consequences. Just 3% dataset poisoning can increase the spam detection error rate of an ML model from 3% to 24%. Considering that seemingly minor tampering can be fatal, proactive detection efforts are essential.
How to detect poisoned machine learning datasets
The good news is that organizations can take several steps to minimize the potential for poisoning by protecting training data, ensuring dataset integrity, and monitoring for anomalies.
1: Clean your data
Deletion is “cleaning” the training material before it reaches the algorithm. This involves dataset filtering and validation, where someone filters out outliers and outliers. If we come across data that appears suspicious, inaccurate, or false, we remove it.
2: Model monitoring
After deployment, companies can monitor their ML models in real time to ensure they don't suddenly display unintended behavior. If you notice a sharp increase in suspicious reactions or inaccuracies, you may be able to find the cause of your addiction.
Anomaly detection plays an important role here as it helps identify cases of poisoning. One way companies can implement this technology is by creating reference and audit algorithms along with public models for comparison.
3: Source security
Protecting ML datasets is more important than ever, so companies must source data only from trusted sources. Additionally, you need to check the reliability and integrity of your model before training it. This detection method also applies to updates, as attackers can easily compromise previously indexed sites.
4: Update
Regularly cleaning and updating ML datasets mitigates split-view addiction and backdoor attacks. Ensuring that the information the model trains on is accurate, relevant, and complete is a continuous process.
5: Validating user input
Organizations must filter and validate all input to prevent users from changing the behavior of their models with targeted, broad, and malicious contributions. This detection method reduces the damage from injection, split-view poisoning, and backdoor attacks.
Organizations Can Prevent Dataset Addiction
ML dataset poisoning can be difficult to detect, but proactive, coordinated efforts can greatly reduce the likelihood that manipulation will impact model performance. In this way, companies can enhance security and protect the integrity of their algorithms.
Zac Amos is ReHack's Features Editor, covering cybersecurity, AI, and automation.
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