Univrsity Of Tehran Press Journal of Information Technology Management 2980-7972 18 4 2026 10 01 Deep Learning Iris Cryptography: A Deep Learning-Based Image Encryption Framework for Secure Information Management 117 133 108918 10.22059/jitm.2026.108918 EN Awadhesh Kumar Singh Ph.D. Scholar, Department of CSE, Sharda School of Engineering & Technology, Sharda University, Greater Noida-201306 UP, India. Amrit Kumar Agrawal Associate Professor, Department of CSE, Sharda School of Engineering & Technology, Sharda University, Greater Noida-201306 UP, India. Journal Article As high-resolution image data increasingly becomes commonplace in healthcare, surveillance systems, smart environments, and cloud platforms, ensuring image security has become one of the major challenges of our time. In this paper, we develop DL-IrisCrypt, a novel biometric-based image encryption framework in which the ciphertext is associated with the identity of the authorized user. We extract a 2048-bit iris code using Daugman segmentation and Gabor filters, which is then used to generate a key for initializing a six-dimensional hyperchaotic system and processed through SHA-512 and HKDF. To enhance randomness in the key-generation process, we incorporate features extracted from the latent space of a properly trained U-Net autoencoder model. The encryption architecture uses Fisher-Yates permutation, two layers operating with PCG64 in counter mode, and three passes of BLAKE2b cipher-block chaining. Experimental results show that the proposed approach achieves an NPCR of 99.61%, a UACI of 33.46%, and an information entropy of 7.9966 bits/byte, while passing all randomness tests provided by NIST SP 800-22. The framework requires an average of 16 milliseconds for encryption and 188 milliseconds for end-to-end processing on a single GPU and has an effective key space of 2^512. In addition to providing robust information security, DL-IrisCrypt offers features that can benefit organizations handling sensitive visual materials. By combining biometric identification, deep learning, and lightweight encryption mechanisms, the system enables secure management and preservation of the confidentiality of important digital assets while facilitating efficient information sharing with low computational requirements. https://jitm.ut.ac.ir/article_108918_4183f64f3ab1929ac1c244f8fe0b9ba5.pdf