A Minimax-Based Deep Learning Framework for Secure IoT Communication via Steganography
Keywords:
DLSIS, IoT security, image steganography, deep learning, Dragonfly Algorithm, PSNR.Abstract
The rapid expansion of Internet of Things (IoT) deployments has intensified the demand for secure and imperceptible communication methods capable of protecting sensitive information on lightweight devices. Conventional cryptographic techniques, while important, cannot independently provide the level of concealment and reliability required in constrained IoT environments. Many existing steganography and deep learning-based techniques offer partial solutions but still struggle with visual distortion, low embedding efficiency, and limited resilience to attacks. To address these limitations, this work introduces DLSIS, a deep learning-enabled steganography model enhanced with an Optimized Dragonfly Algorithm (ODA) for adaptive pixel selection. The framework employs a convolutional encoder–decoder and 2-bit LSB substitution to embed information effectively while preserving image quality. Experimental evaluation using standard image datasets shows that DLSIS achieves a PSNR of 55.3 dB and consistently surpasses prior methods. These results establish DLSIS as a reliable and efficient approach for secure data transmission and authentication in IoT systems.
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