Quantifying the impact of adversarial attacks on information hiding security with steganography

Abstract

In today's digital landscape, information security has become a critical concern, with adversaries constantly seeking ways to breach systems and compromise sensitive data. Steganography, a technique for covert communication by embedding information within innocuous cover media, has emerged as a potent tool for safeguarding information. However, the rise of adversarial attacks poses a significant threat to the efficacy of steganography. This paper explores the current use and significance of quantifying the impact of adversarial attacks on steganography's information-hiding security. By analyzing the vulnerabilities introduced by such attacks, the aim is to enhance the robustness of steganographic methods and fortify digital communication against malicious intrusions. The research quantifies the impact of adversarial attacks on steganography's information security. Motivated by threats in digital communication, the objectives include creating an adversarial attack framework, selecting techniques, and establishing impact metrics. The methodology uniquely combines adversarial attacks with machine learning to simulate practical scenarios. Results, presented through statistical analyses, tables, and graphs, reveal trade-offs between security and payload capacity, with visual aids enhancing the clarity of experiments. The findings provide insights into adversarial threats, guiding practical improvements in information security. The paper concludes with discussions on results, comparisons, and recommendations for fortifying steganographic systems against adversarial threats.