Adversarial attacks in AI involve manipulating the input data to an AI model in a way that causes the model to make a mistake. These manipulations are often imperceptible to humans but can significantly affect the model’s output. For example, subtly altering the pixels of an image can cause an image recognition model to misclassify it, even though the changes are not noticeable to the human eye.

• Autonomous Vehicles: Ensuring that visual recognition systems cannot be tricked by small alterations to road signs or markings.
• Security Systems: Preventing manipulation of biometric identification systems, such as facial recognition.
• Financial Services: Protecting algorithms that detect fraudulent transactions from being misled by manipulated data.
• Healthcare: Ensuring that diagnostic systems, like those analyzing medical imagery, are robust against attacks aiming to mislead diagnosis.

• Image Recognition Misclassification: Slightly altering pixel values to cause misclassification in image recognition models.
• Autonomous Vehicle Attacks: Modifying road signs in ways that are imperceptible to humans but cause misrecognition by vehicle systems.
• Fraud Detection Manipulation: Crafting financial data inputs to evade detection by fraud detection systems.
• Medical Imaging Confusion: Altering medical images to produce incorrect diagnoses by AI-based systems.

• Adversarial Machine Learning – Wikipedia
• Admissible heuristic