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Implementation of an Image Tampering Detection System with a CMOS Image Sensor PUF and OP-TEE.

Tatsuya Oyama¹, Manami Hagizaki¹, Shunsuke Okura²

¹Graduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu 525-8577, Shiga, Japan.

Sensors (Basel, Switzerland)

|November 27, 2024

Summary

This study introduces a novel image tampering detection system. It uses a physically unclonable function (PUF) for secure key generation and a trusted execution environment (TEE) for robust data authentication in AI image recognition.

Keywords:

CMOS image sensor PUF (CIS-PUF)OP-TEE message authentication code (MAC)physically unclonable function (PUF)reverse fuzzy extractor (RFE)trusted execution environment (TEE)

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Area of Science:

Cybersecurity
Artificial Intelligence
Hardware Security

Background:

Image recognition systems rely on AI analysis of sensor data, making data authenticity crucial.
Physical attacks on image sensors can manipulate data, leading to AI misclassifications.
Message Authentication Codes (MACs) are effective countermeasures for ensuring data integrity.

Purpose of the Study:

To propose and demonstrate an image tampering detection system.
To address the security challenge of authenticating image data from sensors for AI.
To implement secure MAC key generation and verification mechanisms.

Main Methods:

Utilized CMOS Image Sensor-based Physically Unclonable Function (CIS-PUF) technology for MAC key generation.
Employed Trusted Execution Environment (TEE) technology, specifically OP-TEE, for secure MAC verification.
Integrated CIS-PUF and OP-TEE on an ARM processor for a portable, open TEE system.

Main Results:

Successfully demonstrated a system that computes and transmits MACs for captured images using CIS-PUF keys.
Verified the system's capability to perform MAC verification within the secure world of OP-TEE.
Showcased the effectiveness of the proposed system in detecting image tampering.

Conclusions:

The proposed system effectively enhances the security of AI image recognition by ensuring data authenticity.
Combining CIS-PUF for key generation and TEE for verification provides a robust solution against image tampering.
This approach offers a practical method for securing image sensor data in AI applications.