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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. [ABSTRACT FROM AUTHOR]

The enlargement of the prostate gland in the reproductive system of males is considered a form of prostate cancer (PrC). The survival rate is considerably improved with earlier diagnosis of cancer; thus, timely intervention should be administered. In this study, a new automatic approach combining several deep learning (DL) techniques was introduced to detect PrC from MRI and ultrasound (US) images. Furthermore, the presented method describes why a certain decision was made given the input MRI or US images. Many pretrained custom-developed layers were added to the pretrained model and employed in the dataset. The study presents an Equilibrium Optimization Algorithm with Deep Learning-based Prostate Cancer Detection and Classification (EOADL-PCDC) technique on MRIs. The main goal of the EOADL-PCDC method lies in the detection and classification of PrC. To achieve this, the EOADL-PCDC technique applies image preprocessing to improve the image quality. In addition, the EOADL-PCDC technique follows the CapsNet (capsule network) model for the feature extraction model. The EOA is based on hyperparameter tuning used to increase the efficiency of CapsNet. The EOADL-PCDC algorithm makes use of the stacked bidirectional long short-term memory (SBiLSTM) model for prostate cancer classification. A comprehensive set of simulations of the EOADL-PCDC algorithm was tested on the benchmark MRI dataset. The experimental outcome revealed the superior performance of the EOADL-PCDC approach over existing methods in terms of different metrics. [ABSTRACT FROM AUTHOR]

We present a new method for three-dimensional (3-D) visualization and identification of biological microorganisms using partially temporal incoherent light in-fine (PTILI) computational holographic imaging and multivariate statistical methods. For 3-D data acquisition of biological microorganisms, the band-pass filtered white light is used to illuminate a biological sample. The transversely and longitudinally diffracted pattern of the biological sample is magnified by microscope objective (MO) and is optically recorded with an image sensor array interfaced with a computer. Three-dimensional reconstruction of the biological sample from the diffraction pattern is accomplished by using computational Fresnel propagation method. Principal components analysis and nonparametric inference algorithms are applied to the 3-D complex amplitude biological sample for identification purposes. Experiments indicate that the proposed system can be useful for identifying biological microorganisms. To the best of our knowledge, this is the first report on using PTILI computational holographic microscopy for identification of biological microorganisms. Index Terms--Digital holography, holographic interferometry, three-dimensional (3-D) image recognition, three-dimensional (3-D) optical imaging.