Adversarial Attack
2024 | AI Dictionary
What is Adversarial Attack: A technique that manipulates input data to deliberately fool AI models, causing them to make incorrect predictions or decisions.
What is an Adversarial Attack?
An adversarial attack is a technique used to deceive machine learning models by introducing subtle perturbations or modifications to input data. These alterations are often so small that they remain undetectable to human perception but can cause significant errors in model predictions, affecting the model’s performance or compromising its integrity. Adversarial attacks are commonly associated with image recognition systems, where small pixel adjustments can make the model misclassify an image.
Types of Adversarial Attacks
- Evasion Attacks: Modify input data to trick a model into making incorrect predictions (e.g., misclassifying a stop sign as a yield sign in autonomous driving).
- Poisoning Attacks: Corrupt the training data to introduce vulnerabilities during model training , which later affect predictions.
- Model Extraction Attacks: Attempt to recreate the model by querying it, allowing attackers to mimic the target model’s behavior.
Applications of Adversarial Attacks
- Cybersecurity Testing: Used to test the robustness of AI systems by evaluating their vulnerability to malicious inputs.
- Autonomous Vehicles: Exploited in research to assess and improve safety by identifying weaknesses in object detection algorithms.
- Facial Recognition : Employed to bypass security systems that rely on face identification by subtly modifying image inputs.
Example of an Adversarial Attack
A common example of an adversarial attack is altering a few pixels in an image of a panda to make a machine learning model classify it as a gibbon. While the image appears identical to the human eye, the model misinterprets it due to the imperceptible noise, highlighting the need for robust defense mechanisms against such attacks.
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