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Machine-learning attacks on interference-based optical encryption: experimental demonstration.

Lina Zhou, Yin Xiao, Wen Chen

    Optics Express
    |September 13, 2019
    PubMed
    Summary

    Machine learning models can now attack interference-based optical encryption. Unauthorized users can retrieve plaintexts from ciphertexts without needing encryption keys, revealing system vulnerabilities.

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    Area of Science:

    • Optics and Cryptography
    • Information Security
    • Machine Learning Applications

    Background:

    • Optical encryption offers advantages but remains vulnerable to attacks.
    • Interference-based optical encryption systems are a key area of development.
    • Conventional cryptanalysis often requires specific conditions or keys.

    Purpose of the Study:

    • To experimentally demonstrate machine learning attacks on interference-based optical encryption.
    • To show that plaintexts can be retrieved without optical encryption keys.
    • To analyze the vulnerability of optical encryption systems using novel methods.

    Main Methods:

    • Utilizing machine learning models trained on ciphertext-plaintext pairs.
    • Applying these models to retrieve unknown plaintexts from given ciphertexts.
    • Estimating system transfer functions or point spread functions without subsidiary conditions.

    Main Results:

    • Successful experimental demonstration of machine learning-based attacks.
    • Retrieval of unknown plaintexts from optical encryption ciphertexts.
    • Effective estimation of system parameters crucial for cryptanalysis.

    Conclusions:

    • Machine learning presents a versatile and effective approach to attacking optical encryption.
    • This method bypasses the need for traditional optical encryption keys.
    • The findings highlight significant vulnerabilities in current interference-based optical encryption.