Image Forgery Detection Based on Semantic Image Understanding

摘要

Image forensics has been focusing on low-level visual features, paying little attention to high-level semantic information of the image. In this work, we propose the framework for image forgery detection based on high-level semantics with three components of image understanding module, the normal rule bank (NR) holding semantic rules that comply with our common sense, and the abnormal rule bank (AR) holding semantic rules that don’t. Ke et al. [1] also proposed a similar framework, but ours has following advantages. Firstly, image understanding module is integrated by a dense image caption model, with no need for human intervention and more hierarchical features. secondly, our proposed framework can generate thousands of semantic rules automatically for NR. Thirdly, besides NR, we also propose to construct AR. In this way, not only can we frame image forgery detection as anomaly detection with NR, but also as recognition problem with AR. The experimental results demonstrate our framework is effective and performs better.

类型
出版物
Communications in Computer and Information Science
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。