Identifying Poisoned Data in Machine Learning Datasets: A Guide

Nearly anyone has the ability to taint a machine learning (ML) dataset to modify its behavior and output significantly and permanently. By implementing vigilant, anticipatory identification efforts, organizations could preserve weeks, months, or even years of work they would otherwise spend rectifying the harm caused by tainted data sources.

What makes data poisoning important?

Data poisoning is a form of adversarial ML attack that maliciously interferes with datasets to deceive or confound the model. The objective is to prompt it to react inaccurately or function in unintended manners. Realistically, this threat could jeopardize the future of AI.

As AI implementation expands, data poisoning becomes more prevalent. Model hallucinations, inappropriate responses, and misclassifications caused by deliberate manipulation have become more frequent. Public trust is already eroding — only 34% of people have absolute faith in technology companies for AI governance.

Instance of machine learning dataset poisoning

Even if an attacker cannot gain access to the training data, they can still disrupt the model by capitalizing on its adaptability to modify its behavior. They could input thousands of targeted messages simultaneously to skew its classification process. Google faced this a few years ago when attackers initiated millions of emails at once to confuse its email filter, causing it to misidentify spam mail as legitimate correspondence.

In another real-world scenario, user input permanently changed an ML algorithm. Microsoft launched its new chatbot “Tay” on Twitter in 2016, attempting to mimic a teenage girl’s conversational style. After only 16 hours, it had posted more than 95,000 tweets — most of which were hateful, discriminatory, or offensive. The company promptly discovered that people were mass-submitting inappropriate input to alter the model’s output.

Common techniques of dataset manipulation

The first category pertains to dataset tampering, where someone maliciously modifies training material to impact the model’s performance. An injection attack — where an attacker inserts inaccurate, offensive, or misleading data — is a typical example.

The necessity of anticipatory identification efforts

Regarding data poisoning, being proactive is crucial to safeguarding an ML model’s integrity. Unintended behavior from a chatbot can be offensive or derogatory, but poisoned cybersecurity-related ML applications have much more severe implications.

Approaches to identify a tainted machine learning dataset

1: Data verification

Verification aims to “cleanse” the training material before it reaches the algorithm by filtering out anomalies and outliers.

2: Model monitoring

Monitoring the ML model in real time after deployment helps ensure that it doesn’t suddenly display unintended behavior and allows businesses to look for the source of the poisoning.

3: Source protection

Securing ML datasets has become more critical than ever.

4: Data updates

Regularly sanitizing and updating an ML dataset mitigates split-view poisoning and backdoor attacks.

5: Validation of user input

Filtering and validating all input to prevent users from altering a model’s behavior with targeted, widespread, malicious contributions is vital.

Organizations can preclude dataset poisoning 

Though ML dataset poisoning can be challenging to detect, a proactive, coordinated effort can significantly reduce the likelihood that manipulations will affect model performance. This way, enterprises can strengthen their security and preserve their algorithm’s integrity.

Zac Amos is features editor at ReHack, where he covers cybersecurity, AI, and automation.

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