Unsupervised Steganalysis Based on Artificial Training Sets
In this paper, an unsupervised steganalysis method that combines artificial training setsand supervised classification is proposed. We provide a formal framework for unsupervisedclassification of stego and cover images in the typical situation of targeted steganalysis (i.e.,for a known algorithm and approximate embedding bit rate). We also present a completeset of experiments using 1) eight different image databases, 2) image features based on RichModels, and 3) three different embedding algorithms: Least Significant Bit (LSB) matching,Highly undetectable steganography (HUGO) and Wavelet Obtained Weights (WOW). Weshow that the experimental results outperform previous methods based on Rich Models inthe majority of the tested cases. At the same time, the proposed approach bypasses theproblem of Cover Source Mismatch -when the embedding algorithm and bit rate are known-, since it removes the need of a training database when we have a large enough testing set.Furthermore, we provide a generic proof of the proposed framework in the machine learningcontext. Hence, the results of this paper could be extended to other classification problemssimilar to steganalysis.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationSteganalysisSimilar Papers 제목 키워드 기반
Pseudo-label Based Domain Adaptation for Zero-Shot Text Steganalysis
Currently, most methods for text steganalysis are based on deep neural networks (DNNs). However, in real-life scenarios, obtaining a sufficient amount of labeled stego-text for correctly training networks using a large n…
Domain AdaptationPseudo LabelSteganalysisZero-Shot Interpretable Image Steganalysis for Invertible Image Hiding
Image steganalysis, which aims at detecting secret information concealed within images, has become a critical countermeasure for assessing the security of steganography methods, especially the emerging invertible image h…
Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions
Steganography and steganalysis are two interrelated aspects of the field of information security. Steganography seeks to conceal communications, whereas steganalysis is aimed to either find them or even, if possible, rec…
Deep LearningDeep Reinforcement LearningSteganalysisTransfer LearningA One-dimensional HEVC video steganalysis method using the Optimality of Predicted Motion Vectors
Among steganalysis techniques, detection against motion vector (MV) domain-based video steganography in High Efficiency Video Coding (HEVC) standard remains a hot and challenging issue. For the purpose of improving the d…
SteganalysisIStego100K: Large-scale Image Steganalysis Dataset
In order to promote the rapid development of image steganalysis technology, in this paper, we construct and release a multivariable large-scale image steganalysis dataset called IStego100K. It contains 208,104 images wit…
Steganalysis