VStegNET: Video Steganography Network using Spatio-Temporal features and Micro-Bottleneck
VStegNET: Video Steganography Network using Spatio-Temporal features and Micro-Bottleneck
复制标题
DOI：
--
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Aayush Mishra;Suraj Kumar;Aditya Nigam;Saiful Islam
中科院分区：
文献类型：
--
作者：
Aayush Mishra;Suraj Kumar;Aditya Nigam;Saiful Islam
隐写术是将秘密信息隐藏在封面信息中，使得隐藏后的封面信息难以辨认，只有目标接收者才能从中提取秘密信息。传统的图像隐写技术将秘密信息隐藏在封面图像的高频区域。这些技术通常导致较低的嵌入率和容易检测。在本文中，我们提出了VStegNET，这是一种视频隐写网络，它使用3D-CNN和微瓶颈（沙漏）提取时空特征，这是视频隐写文献中的第一个。提出的网络将M × N（RGB）个秘密视频帧隐藏到相同大小的覆盖视频帧中。我们在UCF 101动作识别视频数据集上训练了我们的模型，并使用各种定量指标（APD，PSNR和SSIM）评估了其性能，并将其与以前的最先进的模型进行了比较。此外，我们还进行了详细的分析，支持该建议优于图像隐写模型。最后，几个标准的隐写分析工具，如StegExpose，SRNET等，已被用来证明VStegNET的隐写能力。
Steganography is the practice of hiding a secret message in a cover message such that the cover stays indiscernible after hiding and only the intended recipients can extract the secret from it. Traditional image steganography techniques hide the secret image into high-frequency regions of the cover images. These techniques typically result in lower embedding ratios and easy detection. In this paper, we propose VStegNET, a video steganography network that extracts spatio-temporal features using 3D-CNN and micro-bottleneck (Hourglass) which is the first of its kind in the literature of video steganography. The proposed network hides M × N (RGB) secret video frames into same sized cover video frames. We have trained our model on UCF 101 action recognition video dataset and evaluated its performance using various quantitative metrics (APD, PSNR, and SSIM) and compared it with previous the state-of-the-art. Furthermore, we have also presented a detailed analysis, supporting the proposal’s superiority over image steganography models. Finally, several standard steganalysis tools like StegExpose, SRNET, etc. have been used to justify the steganographic capabilities of VStegNET.