👉 Contamination computing, also known as contamination or adversarial attacks, refers to a class of security threats where malicious actors intentionally introduce subtle perturbations or "contaminants" into data, models, or systems to deceive or disrupt their intended functionality. These contaminants are often imperceptible to humans but can significantly alter the behavior of machine learning models, causing them to make incorrect predictions or decisions. For instance, in image recognition tasks, carefully crafted adversarial examples can lead a model to misclassify benign images as harmful ones. Similarly, in natural language processing, slight modifications to text inputs can cause chatbots or sentiment analysis tools to produce erroneous outputs. Contamination computing highlights the vulnerability of AI systems and underscores the need for robust defenses to ensure the reliability and security of these technologies.
