What is an adversarial attack in AI?

What is an adversarial attack in AI?

• A. Natural noise in data.
• B. Intentional manipulation of input data to mislead AI models.
• C. Random hardware fault.
• D. Data encryption technique.

Answer: (B)

An adversarial attack is when someone intentionally crafts inputs to fool an AI model into giving a wrong or biased result, even though the input may look harmless or normal to a human. The crucial point is the deliberate manipulation with the goal of misleading the model, not random or accidental noise. For example, tiny, carefully chosen changes to an image can cause a classifier to mislabel it, while a human would still recognize the object correctly. That distinguishes adversarial attacks from natural noise, which is random and unplanned, or from a random hardware fault, which stems from system errors rather than purposeful manipulation. Data encryption is about protecting information, not altering how a model behaves, so it’s not an adversarial attack. Understanding this helps in governance and risk management, since defenses like robust training, input validation, and anomaly detection are needed to mitigate such targeted manipulations.

An adversarial attack is when someone intentionally crafts inputs to fool an AI model into giving a wrong or biased result, even though the input may look harmless or normal to a human. The crucial point is the deliberate manipulation with the goal of misleading the model, not random or accidental noise. For example, tiny, carefully chosen changes to an image can cause a classifier to mislabel it, while a human would still recognize the object correctly. That distinguishes adversarial attacks from natural noise, which is random and unplanned, or from a random hardware fault, which stems from system errors rather than purposeful manipulation. Data encryption is about protecting information, not altering how a model behaves, so it’s not an adversarial attack. Understanding this helps in governance and risk management, since defenses like robust training, input validation, and anomaly detection are needed to mitigate such targeted manipulations.