Secure Deep Learning for Intelligent Terahertz Metamaterial Identification

Abstract

Metamaterials, artificially engineered structures with extraordinary physical properties, offer multifaceted capabilities in interdisciplinary fields. To address the looming threat of stealthy monitoring, the detection and identification of metamaterials is the next research frontier but have not yet been explored. Here, we show that the crypto-oriented convolutional neural network (CNN) makes possible the secure intelligent detection of metamaterials in mixtures. Terahertz signals were encrypted by homomorphic encryption and the ciphertext was submitted to the CNN directly for results, which can only be decrypted by the data owner. The experimentally measured terahertz signals were augmented and further divided into training sets and test sets using 5-fold cross-validation. Experimental results illustrated that the model achieved an accuracy of 100% on the test sets, which highly outperformed humans and the traditional machine learning. The CNN took 9.6 s to inference on 92 encrypted test signals with homomorphic encryption backend. The proposed method with accuracy and security provides private preserving paradigm for artificial intelligence-based material identification.

A review on machine learning and deep learning for various antenna design applications

“…Every specification of the pre-coders used to obtain the optimum decoder is regarded as a mapping relation in the deep neural network (DNN) in the [ 39 ] solution. In this study, a deep learning assisted mmWave massive MIMO architecture was employed for practical hybrid pre-coding.…”

“… Antenna Used Algorithm Used Compared to Result [ 37 ] PCA THz DL-CT THz CT It shows much superior image quality. [ 38 ] N B, N u, N R RFC SVM Reduce the computational complexity hybrid beamforming [ 39 ] Photoconductive CNN SVM Developing identification of metamaterials in mixtures [ 40 ] Multi-mode multiple antenna DNN Demo of 6G mobile network [ 41 ] UM-MIMO DNN Plasmonic antennas, PCA Future vision of THz communication …”

“…This ciphertext can also be decrypted independently.

Figure 8 Workflow of private preserving THz metamaterial identification [ 39 ].

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Intelligent metaphotonics empowered by machine learning

“…We notice also that ML may be used not only to design and supplement metasensors, but also to sense the presence of metamaterials per se. As an example, a DL algorithm could process electromagnetic response signals obtained with THz-band time-domain spectroscopy in order to identify metamaterial in lactose mixtures [140]. The accuracy reached an astonishing value of 100%, while classical ML algorithm (support vector machine) allowed only 87.9% and human's ability to recognize the spectrum of a metamaterial was below 57%.…”

Inverse design of electromagnetically induced transparency metamaterials based on generative adversarial networks

“…With the continuous development of artificial intelligence, some researchers have tried to apply the feature of the neural network method to the structural design of metamaterials, while can directly extract the implicit connection between data and data, to skip the complex internal solving process between material structure and physical properties [5][6][7][8]. Once the designer has determined the material's physical properties and general shape , the trained network model can instantly derive the appropriate structural parameters or specific model shapes, to substantially reduce the hashrate and time required for later material structure optimization.…”