Detecting double compression for JPEG images of low quality factor

2019 ◽  
Vol 28 (03) ◽  
pp. 1
Author(s):  
Huiling Yuan ◽  
Bo Ou ◽  
Huawei Tian
Keyword(s):  
Quality Factor ◽  
Jpeg Images ◽  
Double Compression

Detecting Non-Aligned Double JPEG Compression Based on Amplitude-Angle Feature

2021 ◽  
Vol 17 (4) ◽  
pp. 1-18
Author(s):  
Jinwei Wang ◽  
Wei Huang ◽  
Xiangyang Luo ◽  
Yun-Qing Shi ◽  
Sunil Kr. Jha
Keyword(s):  
Quality Factor ◽  
Color Image ◽  
Binary Classification ◽  
Image Editing ◽  
Blocking Artifacts ◽  
Jpeg Images ◽  
Compressed Images ◽  
Editing Operation ◽  
Double Jpeg Compression ◽  
Extraction Scheme

Due to the popularity of JPEG format images in recent years, JPEG images will inevitably involve image editing operation. Thus, some tramped images will leave tracks of Non-aligned double JPEG ( NA-DJPEG ) compression. By detecting the presence of NA-DJPEG compression, one can verify whether a given JPEG image has been tampered with. However, only few methods can identify NA-DJPEG compressed images in the case that the primary quality factor is greater than the secondary quality factor. To address this challenging task, this article proposes a novel feature extraction scheme based optimized pixel difference ( OPD ), which is a new measure for blocking artifacts. Firstly, three color channels (RGB) of a reconstructed image generated by decompressing a given JPEG color image are mapped into spherical coordinates to calculate amplitude and two angles (azimuth and zenith). Then, 16 histograms of OPD along the horizontal and vertical directions are calculated in the amplitude and two angles, respectively. Finally, a set of features formed by arranging the bin values of these histograms is used for binary classification. Experiments demonstrate the effectiveness of the proposed method, and the results show that it significantly outperforms the existing typical methods in the mentioned task.

scholarly journals About JPEG Images Parameters Impact to Steganalys Accuracy

2021 ◽  
pp. 74-85
Author(s):  
N. Koshkina
Keyword(s):  
Machine Learning ◽  
Quality Factor ◽  
Detection Accuracy ◽  
Image Preprocessing ◽  
Jpeg Images ◽  
Training Samples ◽  
Wide Range ◽  
Dct Coefficients ◽  
And Control ◽  
Intelligent Computer

Introduction. Existing examples of illegal use of computer steganography prove the need for the development of stegananalytical methods and systems as one of the most important areas of cybersecurity. The advantage of machine learning-based stegananalytical methods is their versatility: they do not rely on knowledge of the injection algorithm and can be used to detect a wide range of steganographic methods. However, before being used for detecting steganocontainers, the methods mentioned require training on containers that are determined for sure whether they contain hidden messages or not. On this stage, it is very important to understand how the parameters of containers under investigation, in particular, such a common variant as JPEG images, affect the accuracy of steganalysis. After all, the inconsistency of the source of containers is an open problem of steganalysis leading to significant decrease of accuracy of detecting hidden messages after the classifier is moved from the laboratory to the real world. The purpose of the work is investigation of influence of the content, size and quality factor of JPEG images to the accuracy of their steganalysis performed by statistical methods based on machine learning. Results. During the research the following patterns were revealed: 1) the accuracy is better when images with a close percentage of coefficients suitable for DCT concealment are used for training and control, 2) images are classified more accurately when they have a relatively small number of suitable DCT coefficients, 3) with using mixed training samples (by content or parameters) the accuracy of steganalysis deteriorates, 4) decreasing quality factor of JPEG-images leads to increasing the accuracy of their steganalysis, 5) increasing size of images increases the accuracy of their steganalysis, 6) images where desynchronization of blocks took place during preprocessing are classified more accurately, 7) the sequence of the image preprocessing operations affects the accuracy of its steganoanalysis. Conclusions. For steganography tasks – the choice of JPEG containers, taking into account revealed patterns, makes steganographic hides more resistant to passive attacks. Considering them for tasks of steganalysis allows one to interpret the obtained results more accurately. Keywords: information security, steganography, stegananalysis, intelligent computer systems, machine learning, detection accuracy.

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