Securing face images in UAV networks using chaos and DNA cryptography approach - Saba Inam; Shamsa Kanwal; Eaman Amin; Omar Cheikhrouhou; Monia Hamdi (Scientific Reports)
Abstract In future applications of UAVs, the frequent transmission of image data raises significant security concerns, particularly regarding the privacy of facial information. Existing methods for protecting such data are often inadequate, especially in the context of UAVs, which require both high security and efficiency. To address these challenges, this paper proposes a face encryption scheme utilizing a 4D hyperchaotic Chen system and DNA cryptography. First, edge recognition face detection technology is employed to detect facial features, with the corresponding matrix selected for encryption. The eigenvalues of the selected matrix are then extracted and hashed using the SHA-256 hash algorithm to enhance security, generating plain image associated chaotic sequences. These sequences are used for Multi-Dimensional cryptic transformation. By employing plaintext-associated hyperchaotic sequences, ciphertext feedback method and dynamic DNA chain encryption, the proposed face encryption scheme significantly strengthens resistance against cryptographic attacks. Simulations were conducted using MATLAB and the resulting performance evaluation metrics were compared with the current state-of-the-art (SOTA) schemes. Experimental outcomes and security analysis confirm that this face encryption scheme provides robust security and high efficiency, as evidenced by key performance metrics specifically an information entropy value of 7.9992, MSE of 8908.61, PSNR of 8.9619, SSIM of 0.00091, NPCR of 99.7163%, UACI of 33.5671%, demonstrating the robustness of our encryption algorithm against differential attacks, and a key space of $$\:\:{2}^{1024}$$ , ensuring resistance against brute-force attacks. Thus, the proposed face encryption scheme offers excellent performance and holds promising potential for ensuring face privacy by preventing unauthorized access to facial information.