18147763. SYSTEM AND METHOD FOR MANAGING AI MODELS USING VIEW LEVEL ANALYSIS simplified abstract (Dell Products L.P.)
Contents
- 1 SYSTEM AND METHOD FOR MANAGING AI MODELS USING VIEW LEVEL ANALYSIS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEM AND METHOD FOR MANAGING AI MODELS USING VIEW LEVEL ANALYSIS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about AI Model Security
- 1.13 Original Abstract Submitted
SYSTEM AND METHOD FOR MANAGING AI MODELS USING VIEW LEVEL ANALYSIS
Organization Name
Inventor(s)
OFIR Ezrielev of Be'er Sheva (IL)
AMIHAI Savir of Newton MA (US)
SYSTEM AND METHOD FOR MANAGING AI MODELS USING VIEW LEVEL ANALYSIS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18147763 titled 'SYSTEM AND METHOD FOR MANAGING AI MODELS USING VIEW LEVEL ANALYSIS
Simplified Explanation
Methods and systems for managing attacks on AI models using view level analysis are disclosed. Malicious parties can introduce poisoned training data to attack AI models, but countermeasures can be designed based on the attacker's view level.
- AI models are updated over time with new training data.
- Snapshots of AI models can be obtained, containing information on training data, parameters, and inferences.
- Malicious parties can attack AI models with poisoned training data.
- Countermeasures can be designed based on the attacker's view level.
Key Features and Innovation
- Managing attacks on AI models using view level analysis.
- Designing countermeasures based on the attacker's view level.
Potential Applications
This technology can be applied in various industries where AI models are vulnerable to attacks, such as cybersecurity, finance, healthcare, and autonomous vehicles.
Problems Solved
This technology addresses the problem of protecting AI models from attacks using poisoned training data.
Benefits
- Enhanced security for AI models.
- Improved resilience against malicious attacks.
- Increased trust in AI systems.
Commercial Applications
- Cybersecurity companies can use this technology to protect AI systems from attacks.
- Finance companies can safeguard their AI models from malicious manipulation.
- Healthcare organizations can ensure the integrity of AI-powered medical devices.
- Autonomous vehicle manufacturers can enhance the security of their AI algorithms.
Prior Art
Readers can explore prior research on AI model security, data poisoning attacks, and view level analysis in the field of artificial intelligence.
Frequently Updated Research
Stay informed about the latest advancements in AI model security, data poisoning prevention, and countermeasures against malicious attacks.
Questions about AI Model Security
How can AI models be protected from data poisoning attacks?
AI models can be safeguarded by implementing robust security measures, such as view level analysis and countermeasures based on the attacker's perspective.
What are the potential implications of AI model attacks in various industries?
AI model attacks can have severe consequences in industries like cybersecurity, finance, healthcare, and autonomous vehicles, leading to financial losses, compromised data integrity, and safety risks.
Original Abstract Submitted
Methods and systems for managing an attack on an artificial intelligence (AI) model using view level analysis are disclosed. As AI models are updated over time using new training data, snapshots of the AI models may be obtained. The snapshots may include information regarding the training data used to train the AI model, the parameters of the AI model, and/or the inferences obtained from the AI model. A malicious party may perform an attack on the AI model by introducing poisoned training data through a data source. The content of supplied poisoned training data may be determined based on a view level into the AI model. The view level of the malicious party may be used to design countermeasures to mitigate and/or prevent future attacks to the AI model.
