Intelligent Hybrid Black-Box Steganography: An AI-Enhanced Approach for Secure Data Embedding

The need for robust information security has never been more urgent, given the proliferation of digital data and sophisticated cyber-attacks. Steganography, the art of concealing information within seemingly benign media, has emerged as a powerful tool for secure communication. However, traditional steganographic techniques, while effective, are becoming increasingly vulnerable to modern steganalysis methods. Attackers and analysts are leveraging advanced tools to detect hidden data within media files, compromising the security and confidentiality of sensitive information. This project aims to tackle these challenges by introducing a novel approach: Hybrid Black-Box Steganography Enhanced with Artificial Intelligence (AI).

In traditional steganography, static algorithms are used to embed data, often leading to predictable patterns that adversaries can exploit. To address this limitation, this project propose a hybrid model that combines multiple steganographic techniques, such as spatial domain, frequency domain, and transform domain methods. These methods will be applied dynamically and intelligently based on the characteristics of the cover media. By using a combination of techniques, the system can increase resilience against detection while optimizing data embedding for different media types.

The true innovation of this project lies in the integration of Artificial Intelligence (AI) to enhance steganographic processes. AI, particularly machine learning, can be utilized to analyze patterns in both the cover media and hidden information, enabling the system to make intelligent decisions on how to apply steganographic methods. The AI component allows for real-time adaptability, where the system can respond to varying media types, data sizes, and security needs without requiring human intervention.

The black-box aspect of this project refers to a design approach where the internal processes and mechanisms of the system are kept concealed from users and external parties. This means that while the system functions efficiently and securely, its internal decision-making, algorithms, and techniques remain hidden from both authorized users and potential attackers. This approach enhances security because it prevents adversaries from understanding or reverse-engineering the method by which data is hidden, making it significantly harder to detect or breach the system.

To validate the effectiveness of this Hybrid Black-Box Steganography system, we will conduct a series of evaluations, focusing on key metrics such as imperceptibility, payload capacity, and resilience to steganalysis.

In conclusion, this project aims to develop an advanced steganography system that leverages the strengths of hybrid models and artificial intelligence to create a secure, adaptive, and intelligent solution for concealing sensitive information.