LiteStegNet: A Lightweight Deep Learning Framework for Video Steganography in IoT-Based Systems

Embedding secret messages within videos makes video steganography one of the most powerful techniques used to protect communications in IoT systems. Unfortunately, the traditional deep learning approaches to steganography are very computationally intensive which makes them impractical for the extremely limited resources available in IoT systems. In this paper, we present LiteStegNet, which is a low-cost deep learning architecture to be used in video steganography in IoT devices. Our video steganography model uses a convolutional autoencoder (CAE) which allows the insertion and retrieval of confidential information with minimum distortion and high fidelity. Through extensive experiments on the UCF101 dataset, we found that LiteStegNet has a peak embedding accuracy of 98.92% and lower processing overhead suitable for real-time IoT applications. Besides, LiteStegNet achieves low reconstruction loss of 0.0140 after the last epoch while SSIM remains over 0.95, indicating high similarity between the original video and the stego-video frames. The proposed framework considerably improves the level of security, efficiency, and computational scalability of steganography in the context of IoT-based multimedia communications.