Image encryption is crucial for ensuring the confidentiality and integrity of digital images, preventing unauthorized access and alterations. However, existing encryption algorithms often involve complex mathematical operations or require specialized hardware, which limits their efficiency and practicality. To address these challenges, we propose a novel image encryption scheme based on the emulation of fundamental quantum operators from a multi-braided quantum group in the sense of Durdevich. These operators—coproduct, product, and braiding—are derived from quantum differential geometry and enable the dynamic generation of encryption values, avoiding the need for computationally intensive processes. Unlike quantum encryption methods that rely on physical quantum hardware, our approach simulates quantum behavior through classical computation, enhancing accessibility and efficiency. The proposed method is applied to grayscale images with 8-, 10-, and 12-bit depth per pixel. To validate its effectiveness, we conducted extensive experiments, including visual quality metrics (PSNR, SSIM), randomness evaluation using NIST 800-22, entropy and correlation analysis, key sensitivity tests, and execution time measurements. Additionally, comparative tests against AES encryption demonstrate the advantages of our approach in terms of performance and security. The results show that the proposed method provides a high level of security while maintaining computational efficiency.

En los últimos años los algoritmos de inteligencia artificial han demostrado tener grandes resultados en diferentes áreas de aplicación, tales como robótica, medicina, seguridad informática, finanzas, entre otras. En el contexto de procesamiento digital de imágenes, el uso del aprendizaje profundo está siendo aplicado para remover marcas de agua visibles en imágenes digitales, con la finalidad de eliminar la protección de derechos de autor de los propietarios de las imágenes en cuestión. El presente trabajo realiza una recopilación de los trabajos más recientes que remueven marcas de agua visibles a través de aprendizaje profundo, con la finalidad de analizar las tendencias actuales que permitan diseñar algoritmos de marcado de agua visible más robustos ante este tipo de herramientas de remoción.

In recent years, several proposals have emerged for executing arithmetic operations using different variants of Spiking Neural P (SNP) systems. However, some of these proposals rely on distinct circuits for each arithmetic operation, while others mandate preliminary configurations for result computation. Recent research suggests that the biological brain decides to activate or inhibit specific neurons based on the operations performed, without prior preparation. Building upon this understanding, the current work introduces a real-number arithmetic SNP circuit capable of dynamically adjusting its behavior without the need for prior configuration. This adaptability is achieved by selecting between addition or subtraction through the utilization of astrocyte-like control and colored spikes. To validate its performance, the circuit was implemented on an FPGA system. The results indicate that the growth in the quantity of 10th-order digits is comparable to recent proposals in terms of hardware usage, requiring fewer neurons than alternative approaches. Moreover, the computation of floating-point numbers enhances the resolution and precision in various arithmetic applications.