隐写术GAN：利用循环生成对抗网络破解隐写术
Steganography GAN: Cracking Steganography with Cycle Generative Adversarial Networks
摘要：
只要人类参与了交流活动，在这些交流媒介中隐藏信息就已经成为一种艺术。通过书面语言或图像的密码信息是一种隐藏手段，通常用于高度敏感或危险的信息。具体来说，密码学领域是防止第三方理解私人消息的协议的构建和分析。隐写术与密码学有关，其目标是使用某种方法或算法来掩盖信息，但最重要的区别是隐写术中的信息和隐藏信息的方法都涉及图像-更准确地说，将一个图像或一段信息嵌入另一个图像。自从创建了隐蔽通信方法以来，已经采取了一些措施来破解密码学和隐写术算法。对这一点的渴望来自人类的好奇心和抵制不利用途的需要，例如在不显眼的媒体中编码有害媒体（钓鱼攻击）。在本文中，我们成功地破解了最低有效位（LSB）隐写算法使用循环生成对抗网络（CycleGANs）和贝叶斯优化，并比较使用CycleGANs对卷积自动编码器。我们的实验结果突出了CycleGANs在破解隐写术方面的前景，并开辟了几条可能的研究途径。
英文摘要：
For as long as humans have participated in the act of communication, concealing information in those communicative mediums has manifested into an art of its own. Crytographic messages, through written language or images, are a means of concealment, usually reserved for highly sensitive or compromising information. Specifically, the field of Cryptography is the construction and analysis of protocols that prevent third parties from understanding private messages. Steganography is related to Cryptography in that the goal is to obscure information using some method or algorithm, but the most important difference is that the information and the method of concealing information within Steganography both involve images--more precisely, the embedding of one image or piece of information into another image. Ever since the creation of covert communication methods, steps have been taken to crack cryptography and steganography algorithms. The desire for this rises from both human curiosity and the need to counteract adverse uses, such as encoding harmful media in inconspicuous media (phishing attack). In this paper, we succeed in cracking the Least Significant Bit (LSB) steganography algorithm using Cycle Generative Adversarial Networks (CycleGANs) and Bayesian Optimization and compare the use of CycleGANs against Convolutional Autoencoders. The results of our experiments highlight the promising nature of CycleGANs in cracking steganography and open several possible avenues of research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
