Deep Steg Block: Deep Learning-Enhanced Steganography for Secure Communication in IoT Devices Using Blockchain

Abstract

DeepStegBlock introduces a cutting-edge framework that combines deep learning-enhanced steganography with blockchain technology for secure and imperceptible communication between Internet of Things devices. DeepStegBlock provides safe and undetectable encrypted message transfer between Internet of Things devices by fusing blockchain technology with deep learning-enhanced steganography. In a time when worries about security lapses and data privacy are on the rise, DeepStegBlock appears to be a powerful remedy that has the potential to completely transform the Internet of Things. DeepStegBlock's clever use of Convolutional Neural Networks allows it to discreetly send sensitive data via Internet of Things networks by hiding encrypted messages inside multimedia content. Combining steganographic methods with CNNs ensures undetectable embedding while maximizing computing capacity, which helps to overcome the limitations imposed by IoT devices. This research leverages the power of Convolutional Neural Networks for the intelligent embedding of encrypted messages into multimedia content, which is then securely transmitted across IoT networks. The incorporation of blockchain ensures immutable recording of data exchanges, providing a dual layer of security through both steganographic techniques and blockchain's ledger system. The framework is specifically designed to address the challenges of limited computational resources in IoT devices, employing lightweight CNN models for efficient real-time processing. DeepStegBlock offers a novel solution for enhancing data privacy and security in the rapidly expanding IoT ecosystem, ensuring that sensitive information remains protected against unauthorized access and tampering. Python is used to implement the suggested system. The suggested system accomplishes blockchain transaction durations of 7 minutes and processing times of 2.5 seconds.