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

Author(s):  
Jinwei Wang ◽  
Wei Huang ◽  
Xiangyang Luo ◽  
Yun-Qing Shi ◽  
Sunil Kr. Jha

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.

2019 ◽  
Vol 28 (03) ◽  
pp. 1
Author(s):  
Huiling Yuan ◽  
Bo Ou ◽  
Huawei Tian

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Hua Zhang ◽  
Jiawei Qin ◽  
Boan Zhang ◽  
Hanbing Yan ◽  
Jing Guo ◽  
...  

The visual recognition of Android malicious applications (Apps) is mainly focused on the binary classification using grayscale images, while the multiclassification of malicious App families is rarely studied. If we can visualize the Android malicious Apps as color images, we will get more features than using grayscale images. In this paper, a method of color visualization for Android Apps is proposed and implemented. Based on this, combined with deep learning models, a multiclassifier for the Android malicious App families is implemented, which can classify 10 common malicious App families. In order to better understand the behavioral characteristics of malicious Apps, we conduct a comprehensive manual analysis for a large number of malicious Apps and summarize 1695 malicious behavior characteristics as customized features. Compared with the App classifier based on the grayscale visualization method, it is verified that the classifier using the color visualization method can achieve better classification results. We use four types of Android App features: classes.dex file, sets of class names, APIs, and customized features as input for App visualization. According to the experimental results, we find out that using the customized features as the color visualization input features can achieve the highest detection accuracy rate, which is 96% in the ten malicious families.


Author(s):  
E. Wilvathi ◽  
M. KOTESWARA RAO

A novel image highly compressed technique has been introduced to reduce the artifacts in compressed JPEG images. In order to reduce the noise, non-linear filtering techniques are often employed than linear filters and don’t degrade the edges. A new metric has been introduced to reduce the artifacts occurred in colored images along the sharp transitions using directional spread parameter. Simulations on compressed images show improvement in artifact reduction by using edge based directional fuzzy filter when compared to the non-linear filters.


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