Adaptive Steganalysis Based on Statistical Model of Quantized DCT Coefficients for JPEG Images

Author(s):  
Tong Qiao ◽  
Xiangyang Luo ◽  
Ting Wu ◽  
Ming Xu ◽  
Zhenxing Qian
2015 ◽  
Vol 64 (1) ◽  
pp. 205-215
Author(s):  
Michala Gulášová ◽  
Matúš Jókay

Abstract The aim of this contribution is to detect the presence of messages in JPEG images that were stored in the files using sequential embedding into the least significant bits of DCT coefficients. The Stegostorage system was used for embedding. For testing purposes, we set this system to the full capacity of cover files as the worst case from the view of security. For steganalysis, we used and implemented a process of calibration of JPEG images, which makes it possible to investigate a histogram from steganographic images which are similar to the histogram of the original images without secret information. Also, Pearson’s Chi- -square test of goodness of fit was implemented, whose results indicate detectability of the presence of hidden messages through images in the two databases, the first database containing 1000 JPEG images and the second one containing 450 JPEG images. An interesting result is that after saving messages into the images and compressing the JPEG once more, the detectability decreased in this case by almost half. Moreover, this contribution provides a framework for further testing by calculating the minimum square error (the weighted stego analysis), which also indicates detectability in both databases, and after embedding the messages into images and re-compressing them with different quantization matrix, the detectability is only slightly reduced. Therefore, we consider this method an appropriate one for further testing and utilizing it when embedding into the LSB randomly, without filling the full capacity of the carrier file.


2014 ◽  
Vol 23 (5) ◽  
pp. 1980-1993 ◽  
Author(s):  
Thanh Hai Thai ◽  
Remi Cogranne ◽  
Florent Retraint

This paper provides a platform to investigate and explore method of ‘partial decoding of JPEG images’ for image classification using Convolutional Neural Network (CNN). The inference is targeting to run on computer system with x86 CPU architecture. We aimed to improve the inference speed of classification by just using part of the compressed domain image information for prediction. We will extract and use the ‘Discrete Cosine Transform’ (DCT) coefficients from compressed domain images to train our models. The trained models are then converted into OpenVINO Intermediate Representation (IR) format for optimization. During inference stage, full decoding is not required as our model only need DCT coefficients which are presented in the process of image partial decoding. Our customized DCT model are able to achieve up to 90% validation and testing accuracy with great competence towards the conventional RGB model. We can also obtain up to 2x times inference speed boost while performing inference on CPU in compressed domain compared with spatial domain employing OpenVINO inference engine.


2010 ◽  
Vol 45 (1) ◽  
pp. 65-74 ◽  
Author(s):  
Matúš Jókay ◽  
Tomáš Moravćík

ABSTRACT This paper deals with the steganographic algorithm LSB (modification of the Least Significant Bits) in JPEG images. The focus is on minimizing of the number of modified DCT coefficients using (2k − 1, 2k − k − 1) Hamming codes. Experimental part of the paper examines the dependencies between the coding, efficiency and saturation.


2018 ◽  
Vol 10 (1) ◽  
pp. 40-53 ◽  
Author(s):  
Shun Zhang ◽  
Liang Yang ◽  
Xihao Xu ◽  
Tiegang Gao

Security always plays an important role in the communication. Steganography, which conceals the process of communication, is another efficient way to achieve secure communication besides encryption. This paper proposes a secure steganography scheme in JPEG images with high embedding capacity and low distortion to the cover image. It embeds the additional information by modifying the DCT coefficients in JPEG images. Considering the size of the additional information, some DCT coefficients are adaptively selected in the embedding process. Two chaotic encryption strategies are designed based on the hyper-chaotic system to encrypt the additional information before the embedding to enhance the security. Extensive experiments have demonstrated the validity and efficiency of this proposed scheme. Compared with some existing schemes, it offers larger embedding rate and lower distortion with stronger security.


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