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Why is No One Talking About Adversarial Machine Learning?
- Adversarial Machine Learning (AML) refers to techniques that are used to exploit the weaknesses of Machine Learning (ML) models creating subtle perturbations in input data to deceive these systems.
- AML can become tools for attackers facilitating sophisticated and automated attacks such as phishing and malware threats.
- AML is a bit difficult to detect as attackers use ML to disrupt machines learning models through techniques like model poisoning, model theft, and adversarial inputs.
- Several possible threats can occur at the Model Deployment Stage such as Evasion Attacks, Poisoning Attacks, Model Extraction Attacks, and Inference Attacks.
- The importance of securing AI systems at every stage of their lifecycle from data collection to building models to deployment is crucial.
- By proactively addressing these challenges, we can ensure AI systems remain trustworthy and reliable.
- Introducing adversarial examples during the training phase can help models learn to resist attacks.
- Continuous vulnerability assessments and penetration testing can identify potential weaknesses in AI systems.
- Building interpretable models allows developers to identify and address unexpected behaviours more effectively.
- Organisations must prioritise security as a foundational element of AI development, not an afterthought.
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