Sonova AG (20240256665). SYSTEM AND METHODS FOR DETECTING A COPY OF A DEEP NEURAL NETWORK IN A HEARING DEVICE simplified abstract

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SYSTEM AND METHODS FOR DETECTING A COPY OF A DEEP NEURAL NETWORK IN A HEARING DEVICE

Organization Name

Sonova AG

Inventor(s)

Sebastian Stenzel of Staefa (CH)

Sebastian Kroedel of Staefa (CH)

Claudio Santelli of Staefa (CH)

Christos Dimopoulos of Zürich (CH)

Harald Krueger of Affoltern am Albis (CH)

SYSTEM AND METHODS FOR DETECTING A COPY OF A DEEP NEURAL NETWORK IN A HEARING DEVICE - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240256665 titled 'SYSTEM AND METHODS FOR DETECTING A COPY OF A DEEP NEURAL NETWORK IN A HEARING DEVICE

    • Simplified Explanation:**

The patent application describes a system and methods for detecting an authentic copy of a deep neural network in a device, such as a hearing device, without reverse engineering the device.

    • Key Features and Innovation:**
  • Detecting unauthorized copies of deep neural networks in devices.
  • Providing audio signals to the deep neural network in the device.
  • Receiving signals triggered by the transmitted audio signals.
  • Determining whether the received signals match the expected signals.
    • Potential Applications:**

This technology can be applied in various industries where deep neural networks are used, such as healthcare, security, and consumer electronics.

    • Problems Solved:**

This technology addresses the issue of unauthorized entities embedding unauthorized copies of deep neural networks in devices, which can compromise the device's functionality and security.

    • Benefits:**
  • Ensures the authenticity of deep neural networks in devices.
  • Enhances the security and reliability of devices using deep neural networks.
  • Prevents unauthorized entities from tampering with deep neural networks in devices.
    • Commercial Applications:**
  • Title: "Enhancing Device Security with Authentic Deep Neural Networks"
  • This technology can be used in hearing aids, smartphones, and other devices that utilize deep neural networks.
  • Market implications include increased trust from consumers and improved product reliability.
    • Questions about the Technology:**

1. How does this technology impact the security of devices using deep neural networks? 2. What are the potential implications of unauthorized copies of deep neural networks in devices?

    • Frequently Updated Research:**

There may be ongoing research in the field of deep neural network authentication and security measures to further enhance the technology described in the patent application.


Original Abstract Submitted

system and methods are presented for detecting an authentic copy of a deep neural network in a device without reverse engineering the device. for example, the device can be a hearing device. a method is presented that includes providing audio signals to a deep neural network located in a hearing device, receiving signals from the deep neural network that are triggered by the transmitted audio signals, and determining whether the received signals were the expected signals. specifically, the system can determine that an unauthorized entity embedded an unauthorized copy of the deep neural network in a hearing device.

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