Deep Steganalysis: End-to-End Learning with Supervisory Information beyond Class Labels

摘要

Recently, deep learning has shown its power in steganalysis. However, the proposed deep models have been often learned from pre-calculated noise residuals with fixed high-pass filters rather than from raw images. In this paper, we propose a new end-to-end learning framework that can learn steganalytic features directly from pixels. In the meantime, the high-pass filters are also automatically learned. Besides class labels, we make use of additional pixel level supervision of cover-stego image pair to jointly and iteratively train the proposed network which consists of a residual calculation network and a steganalysis network. The experimental results prove the effectiveness of the proposed architecture.

出版物
arXiv preprint arXiv:1806.10443
王伟
王伟
副研究员、硕导

主要从事多媒体内容安全、人工智能安全、多模态内容分析与理解等方面的研究工作。

董晶
董晶
研究员、硕导

主要从事多媒体内容安全、人工智能安全、多模态内容分析与理解等方面的研究工作。详情访问:http://cripac.ia.ac.cn/people/jdong

谭铁牛
谭铁牛
研究员,博导

主要从事图像处理、计算机视觉和模式识别等相关领域的研究工作,目前的研究主要集中在生物特征识别、图像视频理解和信息内容安全等三个方向。