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Robust JPEG steganographic algorithms are proposed to protect the embedded message when the covert JPEG image is JPEG-compressed in some lossy channel. They usually perform much better on anti-compression ability than the traditional adaptive JPEG steganographic algorithms. However, massive recent experimental results reveal that the message extraction accuracy of the present robust JPEG steganographic methods are not high enough in some JPEG compressing channels. Thus, this paper proposes a new robust JPEG steganographic algorithm with high message extraction accuracy with equivalent detection resistance. First, the robust channel for the JPEG compression and steganographic embedding are analyzed. Second, a new error correction encoder that can protect the message in lossy channel is proposed. The structure and inside codes of the proposed encoder are different to the traditional codes used in adaptive steganography. Last, the proposed code, the relationship between coefficients and the minimal distortion model are combined to build new steganography. 10,000 images in the popular BOSSbase 1.01 image library are selected for magnanimous experiments. Compared with the adaptive JPEG steganographic algorithm and some current robust JPEG steganographic algorithms, the experimental results show that proposed method can not only resist detection efficiently, but also obtain higher extracting accuracy on resisting JPEG compression. [ABSTRACT FROM AUTHOR]

Steganography detection based on Rich Model features is a hot research direction in steganalysis. However, rich model features usually result a large computation cost. To reduce the dimension of steganalysis features and improve the efficiency of steganalysis algorithm, differing from previous works that normally proposed new feature extraction algorithm, this paper proposes a general steganalysis feature selection method based on decision rough set $\alpha$ -positive region reduction. First, it is pointed out that decision rough set $\alpha$ -positive region reduction is suitable for steganalysis feature selection. Second, a quantization method of attribute separability is proposed to measure the separability of steganalysis feature components. Third, steganalysis feature components selection algorithm based on decision rough set $\alpha$ -positive region reduction is given; thus, stego images can be detected by the selected feature. The proposed method can significantly reduce the feature dimensions and maintain detection accuracy. Based on the BOSSbase-1.01 image database of 10 000 images, a series of feature selection experiments are carried on two kinds of typical rich model features (35263-D J+SRM feature and 17000-D GFR feature). The results show that even though these two kinds of features are reduced to approximately 8000-D, the detection performance of steganalysis algorithms based on the selected features are also maintained with that of original features, which will remarkably improve the efficiency of feature extraction and stego image detection. [ABSTRACT FROM AUTHOR]

Side-informed JPEG steganography is a renowned technology of concealing information for the high resistance to blind detection. The existed popular side-informed JPEG steganographic algorithms use binary embedding method with the corresponding binary distortion function. Then, the embedding methods and binary distortion functions of popular side-informed JPEG steganographic algorithms are analyzed and the wasted secure capacity by using the binary embedding operation is pointed out. Thus, the detection resistance of the side-informed JPEG steganographic algorithms can be improved if the embedding operation is changed to ternary mode which causes less changes than binary embedding at same payload. The problem of using ternary embedding is to define a suitable ternary distortion function. To solve this, a two-dimensional decomposition embedding method is proposed in this paper. The proposed ternary distortion function is defined by transforming the problem into two different binary distortion functions of two layers that based on the ternary entropy decomposition. Meanwhile, the proposed method ensures the minimal value of the distortion function on each layer can be reached in theory. Several popular side-inform JPEG steganographic algorithms (NPQ, EBS, and SI-UNIWARD) are improved through defining ternary distortion function by the proposed method. The experimental results on parameters, blind detection and processing time show that the proposed method increases the blind detection resistance of side-informed steganographic algorithm with acceptable computation complexity. [ABSTRACT FROM AUTHOR]

The article "A robust image steganography based on the concatenated error correction encoder and discrete cosine transform coefficients" written by Zhenkun Bao et al. was originally published electronically on the publisher's internet portal (currently SpringerLink) on 11 June 2019 with open access. [ABSTRACT FROM AUTHOR]