Camera Autoencoder for Secure Image Data Transmission
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Solution Overview
Problem
Conventional techniques for securing camera data, such as encryption, are vulnerable to key interception and do not ensure that the decryption key is unique to a particular camera, potentially allowing unauthorized access to sensitive information.
Innovation Solution
A camera autoencoder is trained using image data from a hardware camera and a fingerprint specific to the camera, which is then divided into an image data encoder stored on the camera and an image data decoder transmitted to a remote computing device. This system encodes and decodes image data in a manner specific to the camera's unique characteristics, ensuring secure transmission and analysis without exposing the data in a visualizable state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional encryption techniques are used to secure camera data, then data security is improved, but the system becomes vulnerable to key interception and unauthorized access
Solution Approach 1:
The patent extracts the decryption capability from the remote system and embeds it directly into the camera device through a neural network model. This eliminates the need to transmit decryption keys separately, as the camera itself performs both encoding and decoding operations using the embedded model, thereby removing the key interception vulnerability while maintaining data security.
Solution Approach 2:
The patent introduces a neural network model as an intermediary that processes camera data locally. Instead of relying on external key management systems, the neural network model acts as a self-contained decryption mechanism embedded within the camera, eliminating the need for key transmission and reducing security vulnerabilities associated with key management.
2Ease of operation
If decryption keys are shared between camera and remote system, then data can be decoded for analysis, but the risk of key interception and unauthorized access increases
Solution Approach 1:
The patent segments the decryption function from the remote system and places it locally within the camera device. The neural network model is divided into encoding and decoding components, both embedded in the camera, allowing data decoding to occur locally without requiring key sharing with external systems, thus eliminating unauthorized access risks while maintaining operational capability.
Solution Approach 2:
The camera system becomes self-sufficient by embedding both encoding and decoding neural network models locally. The device performs all decryption operations independently using its embedded model, eliminating the need for external key management or sharing, thereby reducing unauthorized access risks while maintaining full data decoding capability.
3Loss of information
If camera image data is decrypted for analysis, then useful information can be extracted, but sensitive information may be exposed to anyone with access to the recipient system
Solution Approach 1:
The patent performs preliminary encoding of camera data using a neural network model embedded in the camera before transmission. The data is transformed into an encoded format that cannot be interpreted without the corresponding decoding model, which is also embedded locally. This preliminary encoding action ensures that even if data is accessed during transmission or storage, sensitive information remains protected.
Solution Approach 2:
The patent implements local processing by embedding both encoding and decoding neural network models within the camera device. Data encoding and decoding operations are performed locally rather than relying on external systems, ensuring that sensitive information remains within the secure boundary of the device and is only exposed to authorized local processes, thereby reducing information exposure risks.
Data Source
AI summary
A hardware camera may include a camera sensor configured to determine input image data. The hardware camera may also include an image signal processor configured to perform one or more image signal processing operations on the input image data. The hardware camera may also include a neural processing unit configured to determine encoded image data by encoding the input image data with an image data encoder portion of a camera autoencoder. The camera autoencoder may be trained based on training image data collected from the camera sensor and a fingerprint specific to the hardware camera. The hardware camera may also include a camera communication interface configured to transmit the encoded image data to a remote computing system, which may determine decoded image data by decoding the encoded image data via an image data decoder portion of the camera autoencoder.