论文标题
在线社交网络中用于隐秘传输的图像生成网络
Image Generation Network for Covert Transmission in Online Social Network
论文作者
论文摘要
在线社交网络比以往任何时候都更加激发了互联网的沟通，这使得在此类嘈杂渠道上传输秘密消息是可能的。在本文中，我们提出了一个名为CIS-NET的无封面图像隐志网络，该网络合成了直接在秘密消息上传输的高质量图像。顺式网络由四个模块组成，即产生，对抗，提取和噪声模块。接收器可以提取隐藏的消息而无需任何损失，即使图像已被JPEG压缩攻击扭曲。为了掩盖隐身志的行为，我们在个人资料照片和贴纸的背景下收集了图像，并相应地训练了我们的网络。因此，生成的图像更倾向于摆脱恶意检测和攻击。与先前的图像隐志方法相比，其区别主要是针对各种攻击的鲁棒性和无损性。各种公共数据集的实验表明了抗坚果分析的卓越能力。
Online social networks have stimulated communications over the Internet more than ever, making it possible for secret message transmission over such noisy channels. In this paper, we propose a Coverless Image Steganography Network, called CIS-Net, that synthesizes a high-quality image directly conditioned on the secret message to transfer. CIS-Net is composed of four modules, namely, the Generation, Adversarial, Extraction, and Noise Module. The receiver can extract the hidden message without any loss even the images have been distorted by JPEG compression attacks. To disguise the behaviour of steganography, we collected images in the context of profile photos and stickers and train our network accordingly. As such, the generated images are more inclined to escape from malicious detection and attack. The distinctions from previous image steganography methods are majorly the robustness and losslessness against diverse attacks. Experiments over diverse public datasets have manifested the superior ability of anti-steganalysis.
