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Scalable Neural Cryptanalysis of Block Ciphers in Federated Attack Environments

2025-12-31T15:00:00Z

Title: Scalable Neural Cryptanalysis of Block Ciphers in Federated Attack Environments Author(s): Jeong, Ongee; Park, Seonghwan; Moon, Inkyu Abstract: This paper presents an extended investigation into deep learning-based cryptanalysis of block ciphers by introducing and evaluating a multi-server attack environment. Building upon our prior work in centralized settings, we explore the practicality and scalability of deploying such attacks across multiple distributed edge servers. We assess the vulnerability of five representative block ciphers-DES, SDES, AES-128, SAES, and SPECK32/64-under two neural attack models: Encryption Emulation (EE) and Plaintext Recovery (PR), using both fully connected neural networks and Recurrent Neural Networks (RNNs) based on bidirectional Long Short-Term Memory (BiLSTM). Our experimental results show that the proposed federated learning-based cryptanalysis framework achieves performance nearly identical to that of centralized attacks, particularly for ciphers with low round complexity. Even as the number of edge servers increases to 32, the attack models maintain high accuracy in reduced-round settings. We validate our security assessments through formal statistical significance testing using two-tailed binomial tests with 99% confidence intervals. Additionally, our scalability analysis demonstrates that aggregation times remain negligible (<0.01% of total training time), confirming the computational efficiency of the federated framework. Overall, this work provides both a scalable cryptanalysis framework and valuable insights into the design of cryptographic algorithms that are resilient to distributed, deep learning-based threats.

Simple and practical single-shot digital holography based on unsupervised diffusion model

2025-12-31T15:00:00Z

Title: Simple and practical single-shot digital holography based on unsupervised diffusion model Author(s): Park, Seonghwan; Park, Jaewoo; Kim, Youhyun; Moon, Inkyu; Javidi, Bahram Abstract: Single-shot digital holography in Gabor mode offers cost-effective quantitative phase imaging but suffers from the fundamental twin image problem, where real and conjugate images are inherently superimposed, severely limiting phase reconstruction accuracy. Traditional iterative phase retrieval methods require computationally expensive multiple propagations, while off-axis holography demands complex optical setups with precise alignment. We present the first unsupervised diffusion model for automated phase image reconstruction from single-shot in-line holograms, eliminating both twin image artifacts and the need for expensive off-axis configurations. Our framework integrates cycle-consistency and denoising modules to enable training on unpaired hologram-phase image datasets, learning the mapping between low-cost in-line measurements and high-quality phase distributions without requiring labeled data pairs. Comprehensive evaluation on diverse biological specimens demonstrates that our approach significantly outperforms conventional unsupervised methods, achieving superior Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) values for both red blood cells and cancer cells. Critically, the model maintains exceptional performance even with limited training data, consistently outperforming supervised learning approaches under data-constrained conditions. The framework exhibits remarkable generalization capabilities, successfully reconstructing phase images from holograms captured at different propagation distances and processing various cancer cell types not included in training data. This computational breakthrough enables accurate, scalable, and hardware-efficient quantitative phase imaging, democratizing access to high-quality phase microscopy for resource-constrained environments while maintaining reconstruction fidelity comparable to complex off-axis systems. © 2025 Elsevier B.V., All rights reserved.

Roadmap on Optics and Photonics for Security and Encryption

2025-07-31T15:00:00Z

Title: Roadmap on Optics and Photonics for Security and Encryption Author(s): Javidi, Bahram; Carnicer, Artur; Ahmadi, Kavan; Awatsuji, Yasuhiro; Chen, Wen; Fournel, Thierry; Genevet, Patrice; Guo, Jingying; He, Wenqi; Hebert, Mathieu; Jana, Aloke; Lam, Edmund Y.; Long, Gui-Lu; Matoba, Osamu; Mi, Zhaoke; Moon, Inkyu; Nishchal, Naveen K.; Pan, Dong; Peng, Xiang; Pinkse, Pepijn W. H.; Shi, Yishi; Situ, Guohai; Stern, Adrian; Wang, Xiaogang; Xia, Tian; Xiao, Yin; Zhenwei, Xie; Zhu, Shuo Abstract: In 1994, Javidi and Horner published a paper in Optical Engineering that highlighted the ability of free space optical systems to manipulate sensitive data for authentication purposes. The underlying idea was effective yet surprisingly simple: an optical nonlinear joint transform using a random phase mask in both the input and the reference could produce a correlation peak to indicate whether the input object is authentic or not. This seminal paper fueled the development of this new discipline. After three decades, optical encryption and security have matured into a field that plays a central role in the development of photonics techniques. While the pioneering work was mainly focused on the field of optical information processing, nowadays, a broad spectrum of disciplines are contributing to developing security solutions, including nanotechnology, materials science, quantum information, and deep learning, just to cite a few. The present roadmap paper gathers 28 leading authors in the field from 21 academic institutions across nine different countries. It is organized into 17 sections which discuss the present and future challenges, state-of-the-art technology, and real-world solutions to address the security challenges facing our society.

A Holographic Sensor-Integrated Deep Learning Framework for Noninvasive Assessment of Stored Red Blood Cell Quality

2025-11-30T15:00:00Z

Title: A Holographic Sensor-Integrated Deep Learning Framework for Noninvasive Assessment of Stored Red Blood Cell Quality Author(s): Park, Seonghwan; An, Hyunbin; Rehman, Abdur; Moon, Inkyu Abstract: Prolonged storage of red blood cells (RBCs) induces morphological degradation that can compromise transfusion efficacy. Traditional quality assessment methods are often labor-intensive and time-consuming, limiting their utility in real-time settings. Although deep learning has been applied to RBC imaging, most approaches require large datasets and complex architectures, making them impractical for efficient deployment. This study introduces a holographic sensor-integrated deep learning framework for noninvasive RBC quality assessment using small datasets. A diffusion model is employed to synthetically generate phase images and segmentation masks, augmenting limited data. Self-supervised learning with pre-trained models further enhances classification performance while maintaining a streamlined model architecture. Compared to conventional segmentation methods, the proposed framework achieves higher accuracy and significantly faster inference. It also enables reliable detection of storage-induced morphological changes, providing proportional indicators of transfusion viability. Experimental results validate its effectiveness as a practical tool for real-time, sensor-driven monitoring of RBC quality.