Steganalysis of multi-class JPEG images based on expanded Markov features and polynomial fitting

ACM Trans. Multimedia Comput. Commun. Appl.

2011

Self Cite

This article presents a second-order derivative-based audio steganalysis. First, Mel-cepstrum coefficients and Markov transition features from the second-order derivative of the audio signal are extracted; a support vector machine is then applied to the features for discovering the existence of hidden data in digital audio streams. Also, the relation between audio signal complexity and steganography detection accuracy, which is an issue relevant to audio steganalysis performance evaluation but so far has not been explored, is analyzed experimentally. Results demonstrate that, in comparison with a recently proposed signal stream-based Mel-cepstrum method, the second-order derivative-based audio steganalysis method gains a considerable advantage under all categories of signal complexity-especially for audio streams with high signal complexity, which are generally the most challenging for steganalysis-and thereby significantly improves the state of the art in audio steganalysis.

Section: Second-order Derivative-based Markov Approachmentioning

confidence: 99%

Derivative-based audio steganalysis

ACM Trans. Multimedia Comput. Commun. Appl.

2011

Self Cite

“…To this date, some popular steganographic systems such as LSB embedding, LSB matching, spread spectrum steganography, etc., have been successfully steganalyzed [5,9,10,11,12,13,14,15].…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by Shi et al's work, Pevny and Fridrich merged the DCT features and calibrated Markov transition probabilities to improve the detection performance [17]. Recently, based on Shi et al's work, Chen and Shi [1] and Liu et al [14] respectively expanded the original intra-block Markov approach to inter-block approach. The experimental results demonstrate that the approach integrating intra-block and inter-block Markov transition features outperforms other popular methods [1].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved detection and evaluation for JPEG steganalysis

Proceedings of the 17th ACM International Conference on Multimedia

2009

Self Cite

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.

“…Shi et al [3] proposed a Markov process based approach to detect the information-hiding behaviors in JPEG images. Based on the Markov approach, Liu et al [4] expanded the Markov features to the inter-bands of the DCT domains and combined the expanded features and the polynomial fitting of the histogram of the DCT coefficients, and successfully improved the steganalysis performance in multiple JPEG images. Other works in image steganalysis can be found in the references [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Detecting information-hiding in WAV audios

2008 19th International Conference on Pattern Recognition

2008

Self Cite

In this article, we propose a steganalysis method for detecting the presence of information-hiding behavior in wav audios. We extract the neighboring joint distribution features and the Markov features of the second order derivative, and combine these features with the error response by randomly modifying the least significant bit, then apply learning machines to the features for distinguishing the stegoaudios from cover videos. Experimental results show that our method performs well in steganalysis of the audio stegograms that are produced by using Hide4PGP, Invisible Secrets and S-tools4.