Furniture image protection scheme based on chaotic generating matrix encryption and filter combined with genetic algorithm noise reduction

Abstract Aiming to address the need for intellectual property protection, particularly the risk of digital images being stolen in furniture design, this paper proposes an encryption framework based on the two-dimensional coupled Logistic and Hénon Map (2D-CLH) model. It aims to enhance the security of key digital assets, such as original furniture design drawings. The 2D-CLH exhibits stronger chaotic properties, with a maximum Lyapunov index of 27.4517, sample entropy of 2.2774, and a 0-1 test value of 0.998. These results indicate that the proposed chaotic model is more nonlinear and secure compared to existing chaotic models. Compared with existing scheme frameworks, this scheme introduces a dual dynamic encryption algorithm based on the 2D-CLH model and chaos matrix, combining diffusion and permutation. In the decryption process, adaptive filtering and genetic algorithms are innovatively employed to optimize image distortion. After rigorous testing, the NPCR and UACI of the encryption scheme reached 99.96091% and 33.4662%, respectively, demonstrating the ability to resist brute force attacks. The information entropy of the encrypted image is 7.9994, with pixel distribution approaching the random limit. Even under 0.1 intensity Gaussian noise, the decrypted image can pass the PSNR test (18.62). This method outperforms existing encryption schemes in terms of encryption strength and denoising ability. This study provides a safe and practical technical approach for the digital protection of intellectual property in furniture design.