Robert Boschgmbh

Robert Bosch Engineering and Business Solutions Private Limited

Manojkumar Somabhai Parmar

Sunder Naik Anjali

Shrey Arvind Dabhi

Adit Jignesh Shah

Mayurbhai Thesia Yash

Aithal Himajit

Param Vishweshwar Popat

A method is for checking an integrity of an artificial intelligence (AI) model using distributed ledger technology (DLT). The method leverages state-of-the-art watermarking mechanism and ties it up with DLT to generate proof of origin (provenance) in a tamper-proof way. The AI model is registered on the distributed ledger (DL) by uploading a full checksum, a selective checksum, watermark data, and at least a predefined output of the watermark data. A unique model ID is received upon registration. The ownership and integrity of AI model is then determined by matching the model and the output of the watermark data followed by verification of the full checksum and the selective checksum of the AI model.

Method and a system for checking ownership and integrity of an ai model using distributed ledger technology (DLT)

An AI system and a method of preventing capture of an AI module in the AI system is disclosed. The AI system includes an input interface, a signature verification module, an AI module configured to execute multiple AI models, a hash module, and a key generation module. A signature module and the key generation module provide an output to the user in response to a received input from the user. The AI module further includes an output interface configured to transmit an output response from the signature module to the user.

Method of preventing capture of an AI module and an AI system thereof

An AI system includes an input interface configured to receive input data from at least one user, and a blocker module configured (i) to detect the received input data is an attack input data, (ii) to manipulate output data of the blocker module, and (iii) to send the output data from the blocker module to the at least one user. The AI system further includes an AI module configured to process the input data received by the blocker module and to generate output data corresponding to the input data. The AI system also includes a blocker notification module configured to transmit a notification to the owner of the AI system on detecting an attack input data, and an output interface configured to send the generated output data to the at least one user.

Method of training a module and method of preventing capture of an AI module

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Ahmedabad, India

Manojkumar Somabhai Parmar