Optical triple random-phase encryption

Ahouzi, Esmail; Zamrani, Wiam; Azami, Nawfel; Lizana, Ángel; Campos, Juan; Yzuel, María Josefa

doi:10.1117/1.oe.56.11.113114

Cited by 18 publications

(4 citation statements)

References 43 publications

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“…It can also be utilized in cryptanalysis [16][17][18] to investigate the weaknesses of cryptographic algorithms used for image encryption. In [16], they identified the vulnerability of optical-based cryptographic algorithms, Double Random Phase Encoding (DRPE) [19] and Triple Random Phase Encoding (TRPE) [20], by recovering the encrypted image into the original image with deep learning model based on ResNet [21]. The model was trained to output original image corresponding to the encrypted image fed into the model.…”

Section: Deep Learning-based Cryptanalysis On Image Datamentioning

confidence: 99%

Comprehensive Neural Cryptanalysis on Block Ciphers Using Different Encryption Methods

Jeong,

Ahmadzadeh,

Moon

2024

Preprint

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In this paper, we perform the neural cryptanalysis and comprehensively analyze five block ciphers, Data Encryption Standard (DES), Simplified DES (SDES), Advanced Encryption Standard (AES), Simplified AES (SAES), and SPECK. The vulnerability of the block ciphers is investigated in three different attacks, such as Encryption Emulation (EE), Plaintext Recovery (PR), Key Recovery (KR), and Ciphertext Classification (CC) attacks. For the plaintexts, randomly generated block-sized bit arrays and texts are used. The block ciphers apply different numbers of round functions in the encryption of the block-sized bit arrays, and they are investigated by using the deep learning models trained with different numbers of data in EE, PR, and KR attacks. Moreover, the block ciphers use two different text encryption methods, Word-based Text Encryption (WTE) and Sentence-based Text Encryption (STE), to encrypt the texts in various operation modes, and they are analyzed with deep learning models in EE, PR, and CC attacks. As a result, the block ciphers can be vulnerable to deep learning-based EE and PR attacks using a large amount of train data, and STE can improve the strength of the block ciphers, unlike WTE, which shows almost the same classification accuracy as the plaintexts, especially in CC attack. Additionally, when the keys that are the same as the plaintexts are used in encryption, the block ciphers can be perfectly broken in KR attack. Moreover, especially in KR attack, the RNN-based deep learning model shows higher average Bit Accuracy Probability (BAPavg) than the fully connected-based deep learning model, which is used more often in previous works of neural cryptanalysis. Furthermore, although the transformer-based deep learning model is a state-of-the-art model in Natural Language Processing (NLP), the RNN-based deep learning model is more suitable for CC attack and shows higher classification accuracy.

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Section: Deep Learning-based Cryptanalysis On Image Datamentioning

confidence: 99%

Comprehensive Neural Cryptanalysis on Block Ciphers Using Different Encryption Methods

Jeong,

Ahmadzadeh,

Moon

2024

Preprint

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“…It can also be utilized in cryptanalysis [19][20][21] to investigate the weaknesses of cryptographic algorithms used for image encryption. In [19], they identified the vulnerability of optical-based cryptographic algorithms, Double Random Phase Encoding (DRPE) [22] and Triple Random Phase Encoding (TRPE) [23], by recovering the original image from the encrypted image with a deep learning model based on ResNet [24]. The model was trained to output the original image corresponding to the encrypted image fed into the model.…”

Section: Deep Learning-based Cryptanalysis On Image Datamentioning

confidence: 99%

Comprehensive Neural Cryptanalysis on Block Ciphers Using Different Encryption Methods

Jeong,

Ahmadzadeh,

Moon

2024

Mathematics

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In this paper, we perform neural cryptanalysis on five block ciphers: Data Encryption Standard (DES), Simplified DES (SDES), Advanced Encryption Standard (AES), Simplified AES (SAES), and SPECK. The block ciphers are investigated on three different deep learning-based attacks, Encryption Emulation (EE), Plaintext Recovery (PR), Key Recovery (KR), and Ciphertext Classification (CC) attacks. The attacks attempt to break the block ciphers in various cases, such as different types of plaintexts (i.e., block-sized bit arrays and texts), different numbers of round functions and quantity of training data, different text encryption methods (i.e., Word-based Text Encryption (WTE) and Sentence-based Text Encryption (STE)), and different deep learning model architectures. As a result, the block ciphers can be vulnerable to EE and PR attacks using a large amount of training data, and STE can improve the strength of the block ciphers, unlike WTE, which shows almost the same classification accuracy as the plaintexts, especially in a CC attack. Moreover, especially in the KR attack, the Recurrent Neural Network (RNN)-based deep learning model shows higher average Bit Accuracy Probability than the fully connected-based deep learning model. Furthermore, the RNN-based deep learning model is more suitable than the transformer-based deep learning model in the CC attack. Besides, when the keys are the same as the plaintexts, the KR attack can perfectly break the block ciphers, even if the plaintexts are randomly generated. Additionally, we identify that DES and SPECK32/64 applying two round functions are more vulnerable than those applying the single round function by performing the KR attack with randomly generated keys and randomly generated single plaintext.

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“…The solutions of cryptanalysis are approximate in general and which can be performed for binary or grayscale images [28]. However, some advanced architectures that are immune to attacks based on phase recovery algorithms have been proposed [29,30,31,32,33].…”

Section: Introductionmentioning

confidence: 99%

Ciphertext-only attacks on the double random-phase encryption based on redundancy vulnerability

Chen

Zhang

et al. 2020

Preprint

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Background: The double random phase encoding techniques have received considerable attention from researchers in recent years because of its advantages of parallel and high speed processing capability. Meanwhile, the security of cryptosystem is also one of the major concerns. Methods : We experimentally demonstrated the ciphertext redundancy vulnerability of the coherent double random encryption system (DRPE). Based on the statistical ergodicity of speckles and the consistency of the power spectral density (ESD), we have proved that the most plaintext information can be retrieved from partial ciphertext alone. Results: In this paper, the simulation and experiment result were performed to verify whether the algorithm is effectiveness. The ciphertext redundancy of the DRPE system is analyzed from the results of ciphertext occlusion test. There is a risk of plaintext leakage of this scheme, as long as the average ESD can be estimated from the sub-images. The results will help to open up deeper understanding of limitation of current optical security techniques. Conclusions: The DRPE system has potential redundancy risk. Even one-time-pad manner is not secured in DRPE system. This vulnerability allows a cryptanalyst to estimate the plaintext information with only a half or less ciphertext.

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Optical triple random-phase encryption

Cited by 18 publications

References 43 publications

Comprehensive Neural Cryptanalysis on Block Ciphers Using Different Encryption Methods

Comprehensive Neural Cryptanalysis on Block Ciphers Using Different Encryption Methods

Comprehensive Neural Cryptanalysis on Block Ciphers Using Different Encryption Methods

Ciphertext-only attacks on the double random-phase encryption based on redundancy vulnerability

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