Vulnerability to machine learning attacks of optical encryption based on diffractive imaging

Zhou, Lina; Xiao, Yin; Chen, Wen

doi:10.1016/j.optlaseng.2019.105858

Cited by 37 publications

(9 citation statements)

References 34 publications

(49 reference statements)

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“…End-to-end learning is used in the proposed approach to derive a superior mapping connection between ciphertexts and plaintexts. e suggested learning technique is viable and effective for analyzing the susceptibility of optical encryption systems, as demonstrated by simulations and optical experimental findings [7].…”

Section: Literature Surveymentioning

confidence: 89%

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A New Highly Secure Optical Image Security Technique Using Gyrator Transform for Image Security-Related Applications

Anusree¹,

Rahiman

2022

International Journal of Optics

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New methods and apparatuses for information security have evolved as a result of the rapid expansion of optical information processing. Security is one of the major issues in digital image transmission because it can deliver very secret information to any corresponding agency such as the military, biomedical, and security agencies. Previously, various techniques are proposed to perform optical image encryption techniques using different transformation and pixel-level techniques. Each work has its advantages and disadvantages in terms of computational complexity, security level, flexibility, quality, and so on. To overcome the security issues present in the previous works, a novel optical image encryption standard is proposed in this paper. This work uses information hiding followed by image encryption using Gyrator Transform (GT) using mean gradient key-based block swapping techniques. The main advantage of this work is that the key generation is dynamic and it depends upon the pixel intensity of 8 × 8 blocks. Secret information hiding is performed in the Discrete Cosine Transform (DCT) domain to protect the data against noise attacks. To analyze the performance, various evaluation metrics are used to measure the quality of the decrypted image under various distortions such as cropping and rotation. The robustness of information hiding is analyzed using a noise attack on the received image. This work achieved 45.6 dB of Peak Signal-to-Noise Ratio (PSNR) and 0.965 of Structural Similarity Index (SSIM), which is the best when compared to the conventional image encryption standards.

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Section: Literature Surveymentioning

confidence: 89%

“…en, the "0" value blocks are swapped into the imaginary number blocks, and imaginary number blocks are swapped into real numbers blocks. It is explained in equations ( 6) and (7).…”

Section: Mean Difference Key-based Blockmentioning

confidence: 99%

A New Highly Secure Optical Image Security Technique Using Gyrator Transform for Image Security-Related Applications

Anusree¹,

Rahiman

2022

International Journal of Optics

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“…Image noise is interference data in image data that cause some useful image data to become invisible. The process of image denoising is considered image restoration [ 11 ]. The authors of [ 12 ] proposed a new cross-image, pixel scrambling-based rotation domain, dual-image encryption technique.…”

Section: Related Workmentioning

confidence: 99%

Face Image Encryption Based on Feature with Optimization Using Secure Crypto General Adversarial Neural Network and Optical Chaotic Map

Alsafyani

Alhomayani

Alsuwat

et al. 2023

Sensors

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Demand for data security is increasing as information technology advances. Encryption technology based on biometrics has advanced significantly to meet more convenient and secure needs. Because of the stability of face traits and the difficulty of counterfeiting, the iris method has become an essential research object in data security research. This study proposes a revolutionary face feature encryption technique that combines picture optimization with cryptography and deep learning (DL) architectures. To improve the security of the key, an optical chaotic map is employed to manage the initial standards of the 5D conservative chaotic method. A safe Crypto General Adversarial neural network and chaotic optical map are provided to finish the course of encrypting and decrypting facial images. The target field is used as a "hidden factor" in the machine learning (ML) method in the encryption method. An encrypted image is recovered to a unique image using a modernization network to achieve picture decryption. A region-of-interest (ROI) network is provided to extract involved items from encrypted images to make data mining easier in a privacy-protected setting. This study’s findings reveal that the recommended implementation provides significantly improved security without sacrificing image quality. Experimental results show that the proposed model outperforms the existing models in terms of PSNR of 92%, RMSE of 85%, SSIM of 68%, MAP of 52%, and encryption speed of 88%.

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“…Other papers analyzed flaws and weakness of optical encryption, mainly related with the properties of Fourier transforms [20][21][22]. Very recently, the generalized use of deep learning provides a new perspective for analyzing optical encryption problems (see, for instance, [23][24][25][26][27][28][29][30]). Note that it is practically impossible to cite all relevant work produced in the last years.…”

Section: Introductionmentioning

confidence: 99%

Optical Visual Encryption Using Focused Beams and Convolutional Neural Networks

Ahmadi¹,

Carnicer²

2022

SSRN Journal

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Vulnerability to machine learning attacks of optical encryption based on diffractive imaging

Cited by 37 publications

References 34 publications

A New Highly Secure Optical Image Security Technique Using Gyrator Transform for Image Security-Related Applications

A New Highly Secure Optical Image Security Technique Using Gyrator Transform for Image Security-Related Applications

Face Image Encryption Based on Feature with Optimization Using Secure Crypto General Adversarial Neural Network and Optical Chaotic Map

Optical Visual Encryption Using Focused Beams and Convolutional Neural Networks

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