En-VStegNET: Video Steganography using spatio-temporal feature enhancement with 3D-CNN and Hourglass

Aman Jaiswal, Suraj Kumar, Aditya Nigam

Abstract: Learning Spatio-temporal features has shown improved performance on tasks involving video analysis using deep learning, and the deep learning community has used these features to solve a varied variety of problems. Video steganography is one such problem where learning these features for a video can help improve the performance of steganography. Steganography is the practice of concealing confidential information, to protect the information from an adversary, into an ordinary cover message in a way that the cover message does not seem suspicious to the adversary. Recent deep-learning-based steganography methods have proven to improve the secrecy and capacity of steganography over traditional techniques. In this paper, we propose a novel state-of-the-art deep 3D-CNN architecture with enhancement feature learning for full video steganography. The proposed model outperforms the current state-of-the-art methods for full video steganography both qualitatively and quantitatively. We have validated our model by comparing it with new as well as traditional steganography techniques, on quality and different statistical metrics, namely, PSNR, SSIM, APD, VIF at the frame, and video level. Moreover, to check the undetectability of our model, we have subjected our model to detection by steganalysis tools like SRNet. Results of fine-tuning classifiers, like ResNet and Inception-v3, to detect steganographic messages from ordinary messages maintains our model's undetectability and accuracy.