ADVERSARIAL ATTACK DETECTION AND AVOIDANCE IN COMPUTER VISION

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ADVERSARIAL ATTACK DETECTION AND AVOIDANCE IN COMPUTER VISION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240296225 titled 'ADVERSARIAL ATTACK DETECTION AND AVOIDANCE IN COMPUTER VISION

The patent application discusses techniques for adversarial attack avoidance for machine learning (ML). These techniques involve using trained ML models to predict objects in images while preventing attacks from altering the results.

Trained ML model receives images and attack data.
Predicts objects in images based on images, metadata, and attack data.
Uses metadata to protect against attack data altering predictions.

Potential Applications: - Enhancing the security of machine learning models. - Improving the accuracy and reliability of image recognition systems.

Problems Solved: - Mitigating the impact of adversarial attacks on ML models. - Safeguarding the integrity of image recognition processes.

Benefits: - Increased robustness of ML models against attacks. - Enhanced trust in the predictions made by ML systems.

Commercial Applications: Title: "Secure Image Recognition Technology for Enhanced Data Protection" This technology can be utilized in industries such as cybersecurity, autonomous vehicles, and healthcare for secure and reliable image recognition systems.

Questions about Adversarial Attack Avoidance for Machine Learning: 1. How does this technology contribute to the overall security of machine learning systems? 2. What are the potential implications of this innovation for industries relying on image recognition technology?

Frequently Updated Research: Stay updated on advancements in adversarial attack techniques and defense mechanisms in the field of machine learning to ensure the continued effectiveness of this technology.

Original Abstract Submitted

techniques for adversarial attack avoidance for machine learning (ml) are disclosed. these techniques include receiving one or more images at a trained ml model and receiving attack data at the ml model. the techniques further include predicting an object depicted in the one or more images using the ml model, based on the one or more images, metadata relating to the one or more images, and the attack data. the ml model uses the metadata to prevent the attack data from changing a result of the predicting.