Dell products l.p. (20240111902). DEFENSE AGAINST XAI ADVERSARIAL ATTACKS BY DETECTION OF COMPUTATIONAL RESOURCE FOOTPRINTS simplified abstract
Contents
- 1 DEFENSE AGAINST XAI ADVERSARIAL ATTACKS BY DETECTION OF COMPUTATIONAL RESOURCE FOOTPRINTS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DEFENSE AGAINST XAI ADVERSARIAL ATTACKS BY DETECTION OF COMPUTATIONAL RESOURCE FOOTPRINTS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
DEFENSE AGAINST XAI ADVERSARIAL ATTACKS BY DETECTION OF COMPUTATIONAL RESOURCE FOOTPRINTS
Organization Name
Inventor(s)
Iam Palatnik De Sousa of Rio de Janeiro (BR)
Adriana Bechara Prado of Niteroi (BR)
DEFENSE AGAINST XAI ADVERSARIAL ATTACKS BY DETECTION OF COMPUTATIONAL RESOURCE FOOTPRINTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240111902 titled 'DEFENSE AGAINST XAI ADVERSARIAL ATTACKS BY DETECTION OF COMPUTATIONAL RESOURCE FOOTPRINTS
Simplified Explanation
The abstract of the patent application describes a method for auditing a machine learning model by analyzing its computational resource footprint to detect adversarial attacks.
- Initiating an audit of a machine learning model
- Providing input data to the machine learning model during the audit
- Receiving information on the operation of the machine learning model, including its computational resource footprint
- Analyzing the computational resource footprint to identify characteristics of an adversarial attack
- Determining if the computational resource footprint indicates an adversarial attack on the machine learning model
Potential Applications
This technology could be applied in various industries where machine learning models are used, such as cybersecurity, finance, healthcare, and autonomous vehicles.
Problems Solved
This technology helps in detecting and mitigating adversarial attacks on machine learning models, enhancing their security and reliability.
Benefits
- Improved security of machine learning models - Enhanced trust in the predictions and decisions made by machine learning models - Prevention of malicious attacks on machine learning systems
Potential Commercial Applications
The technology could be utilized by cybersecurity companies, financial institutions, healthcare providers, and companies developing autonomous vehicles to protect their machine learning models from adversarial attacks.
Possible Prior Art
Prior art in this field may include research papers, patents, or technologies that focus on detecting and mitigating adversarial attacks on machine learning models using computational resource analysis.
Unanswered Questions
How does the method handle false positives in detecting adversarial attacks?
The method described in the patent application does not specify how it distinguishes between actual adversarial attacks and false positives in the computational resource footprint analysis.
What are the limitations of the method in detecting sophisticated adversarial attacks?
The patent application does not address the potential limitations of the method in detecting advanced or sophisticated adversarial attacks on machine learning models.
Original Abstract Submitted
one example method includes initiating an audit of a machine learning model, providing input data to the machine learning model as part of the audit, while the audit is running, receiving information regarding operation of the machine learning model, wherein the information comprises a computational resource footprint, analyzing the computational resource footprint, and determining, based on the analyzing, that the computational resource footprint is characteristic of an adversarial attack on the machine learning model.
