Improved detection and evaluation for JPEG steganalysis

  • Authors:
  • Qingzhong Liu;Andrew H. Sung;Mengyu Qiao

  • Affiliations:
  • New Mexico Tech, Socorro, USA;New Mexico Tech, Socorro, USA;New Mexico Tech, Socorro, USA

  • Venue:
  • MM '09 Proceedings of the 17th ACM international conference on Multimedia
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

Detection of information-hiding in JPEG images is actively delivered in steganalysis community due to the fact that JPEG is a widely used compression standard and several steganographic systems have been designed for covert communication in JPEG images. In this paper, we propose a novel method of JPEG steganalysis. Based on an observation of bi-variate generalized Gaussian distribution in Discrete Cosine Transform (DCT) domain, neighboring joint density features on both intra-block and inter-block are extracted. Support Vector Machines (SVMs) are applied for detection. Experimental results indicate that this new method prominently improves a current art of steganalysis in detecting several steganographic systems in JPEG images. Our study also shows that it is more accurate to evaluate the detection performance in terms of both image complexity and information hiding ratio.