Feature Learning for Steganalysis Using Convolutional Neural Networks

摘要

Traditional steganalysis methods usually rely on handcrafted features. However, with the rapid development of advanced steganography, manual design of complex features has become increasingly difficult. In this paper, we propose a new paradigm for steganalysis based on the concept of feature learning. In our method, Convolutional Neural Network (CNN) is used to automatically learn features for steganalysis. To make CNN work better for steganalysis, we incorporate domain knowledge of steganalysis (i.e. enhancing stego noise and exploiting nearby dependencies) when designing the CNN architectures. We further propose to use model combination to boost the performance of CNN based method. Additionally, a cropping strategy is proposed to enable the CNN based model to deal with arbitrary input image sizes. We demonstrate the effectiveness of the proposed method against state-of-the-art spatial domain steganographic algorithms such as HUGO, WOW, S-UNIWARD, MiPOD, and HILL-CMD. To help understand the learned features from CNN, we provide visualizations of the learned filters and feature maps. Finally, we also provide quantitative analysis of the learned features from convolutional layers.

出版物
Multimedia Tools and Applications
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

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。

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。