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Boosting Digital Safeguards: Blending Cryptography and Steganography

arXiv 2404.05985 ↗First posted 9 Apr 2024cs.CR cs.LGcited by 0

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Abstract (arXiv)

In today's digital age, the internet is essential for communication and the sharing of information, creating a critical need for sophisticated data security measures to prevent unauthorized access and exploitation. Cryptography encrypts messages into a cipher text that is incomprehensible to unauthorized readers, thus safeguarding data during its transmission. Steganography, on the other hand, originates from the Greek term for "covered writing" and involves the art of hiding data within another medium, thereby facilitating covert communication by making the message invisible. This proposed approach takes advantage of the latest advancements in Artificial Intelligence (AI) and Deep Learning (DL), especially through the application of Generative Adversarial Networks (GANs), to improve upon traditional steganographic methods. By embedding encrypted data within another medium, our method ensures that the communication remains hidden from prying eyes. The application of GANs enables a smart, secure system that utilizes the inherent sensitivity of neural networks to slight alterations in data, enhancing the protection against detection. By merging the encryption techniques of cryptography with the hiding capabilities of steganography, and augmenting these with the strengths of AI, we introduce a comprehensive security system designed to maintain both the privacy and integrity of information. This system is crafted not just to prevent unauthorized access or modification of data, but also to keep the existence of the data hidden. This fusion of technologies tackles the core challenges of data security in the current era of open digital communication, presenting an advanced solution with the potential to transform the landscape of information security.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 21 references that are in the map. First posted on arXiv 9 Apr 2024.

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2 of 21 references.

References · 2

  1. 2017Steganographic Generative Adversarial Networkscited by 21
  2. 2019Recent Advances of Image Steganography with Generative Adversarial Networkscited by 5

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