Adversarial Attacks and Defences in Machine Learning

As AI and machine learning advance at a rapid pace and many companies pursue the integration of the new technology into their existing systems, there is a subsequent rise in threats to the integrity of an AI-led framework.

One such threat is an Adversarial Attack where the primary goal is to weaken or manipulate the data used by AI to cause mistakes and errors in generated results. Researchers and cyber-security experts must now develop models robust enough to withstand such attacks—and to detect and halt them before any damage occurs.

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What is an Adversarial Attack?

In data-driven systems, inputs are critical and altering their integrity or factuality has ripple effects on both decisions rendered and the system. Algorithms that rely on assessing samples that are largely unvetted and unverified by humans in systems where massive quantities of data are analysed are particularly vulnerable to adversarial attacks, as managers and staff are unable to determine the quality of the data being consumed.

These types of attacks are often organised into three categories: white-box, black-box, and gray-box, depending on the level of knowledge from adversaries. White-box attackers are privy to the entirety of information the target possesses, including model architecture and system parameters. Gray-box threats rely on a limited sampling of information, usually structural, while black-box attackers are operating entirely in the dark and send out queries to grasp the information needed to launch the attack.

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Adversarial attacks of all kind cause damage to operating systems and can seriously degrade the value of their results as well as the performance of the system. The negative impact to the learning process offers no benefit to the network as a whole and measurably decreases both the accuracy of findings and the rate of convergence. Examples include poisoning attacks where malicious data is inserted to alter the patterns developed by AI or transfer attacks where systems designed for one type of program are inserted into another, leading to incorrect conclusions.

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Undermining data can have disastrous effects—defences are needed to ensure these attacks do not happen or are detected before causing damage.

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Defences Against Adversarial Attacks

New defences are currently being created to ensure generative AI and machine learning systems are strong enough to withstand adversarial attacks.

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Machine-learning can be enabled with adversarial training modelling to ensure malicious inputs are immediately detected allowing AI to self-regulate and protect from improper code. Data augmentation enlarges the field of data, creating more opportunities to draw correct conclusions from a large pool rather than relying on smaller data points which can be corrupted.

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Rotation of data continuously reorganises information, which can help identify anomalies or malicious information. Intrusion detection systems are the most applied defence where systems are searched for behavior, network traffic patterns, and system logs that signal an attack is ongoing or has occurred.

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As our technology advances, so too do the threats that can subvert the power and integrity of systems. It is imperative that machine learning, generative AI, and AI-informed systems adhere to strong security systems to maintain their purpose and deliver results.

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About the Author

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Dean Stancevski is a Senior IT Consultant and the founder of DS Technology Consulting Services, offering on-site and remote technical IT services to private and public organisations. A creative problem solver, Dean specialises in helping small- and medium-sized organisations grow by providing customised services to streamline IT systems and operations.