3D mesh steganalysis using local shape features

Zhenyu Li, Adrian G. Bors

Abstract: Steganalysis aims to identify those changes performed in a specific media with the intention to hide information. In this paper we assess the efficiency, in finding hidden information, of several local feature detectors. In the proposed 3D steanalysis approach we first smooth the cover object and its corresponding stego-object obtained after embedding a given message. We use various operators in order to extract local features from both the cover and stego-objects, and their smoothed versions. Machine learning algorithms are then used for learning to discriminate between those 3D objects which are used as carriers of hidden information and those are not used. The proposed 3D steganalysis methodology is shown to provide superior performance to other approaches in a well known database of 3D objects.