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What is Adversarial machine learning?

Adversarial machine learning is a technique used to manipulate AI models by introducing deceptive inputs. These attacks exploit vulnerabilities in models, leading to incorrect predictions or decisions. Adversarial defenses, such as robust training methods, are developed to mitigate these risks.

Data masking
Data masking is a security technique that replaces real data with fictitious yet structurally similar data to prevent unauthorized access while maintaining usability. It is widely used in testing, analytics, and compliance processes to protect sensitive data such as personally identifiable information (PII) and financial records.
Upsampling
Upsampling refers to increasing the resolution or quality of data, particularly in image processing and machine learning. It enhances model accuracy in tasks like super-resolution and data augmentation.
Privacy-enhancing technologies
Privacy-enhancing technologies (PETs) are tools and techniques designed to protect users' personal information while enabling data analysis and processing. These include differential privacy, homomorphic encryption, and secure multi-party computation, commonly used in AI, analytics, and regulatory compliance.

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