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Paper Abstract and Keywords
Presentation 2021-05-21 13:35
A Study on Feature Extraction Suitable for Double JPEG Compression Analysis Based on Statistical Bias Observation of DCT Coefficients
Daichi Takeshita, Minoru Kuribayashi, Nobuo Funabiki (Okayama Univ.) IT2021-12 EMM2021-12
Abstract (in Japanese) (See Japanese page) 
(in English) Pictures taken by smart phones and camera devices are generally compressed by JPEG by default when they are saved. If such an image is edited, it is decompressed and processed, and then it is compressed again by JPEG. So, an edited must be compressed by JPEG more than once. Using this characteristic, a forensic technique has been studied to detect image tampering by detecting distortions caused by double compression. In our previous work, we observed the histogram calculated from some low frequency components in each 8×8 block of 512×512 pixel images to analyze the JPEG compression history using convolutional neural networks (CNN). However, there is no detailed consideration about the range of observed histogram and the selection of DCT coefficients to extract the features from a given image. In this study, we first examine the range of histogram to measure the usefulness for classification of double JPEG compressed images, and then examined the classification accuracy by increasing the number of DCT coefficients observed in the low to mid frequency components.Our experimental results indicated that, $[-40,40]$ was an appropriate range for the observation of histogram, and the selection of DCT coefficients strongly depends on the image size because of the difference in the amount of useful statistical information.
Keyword (in Japanese) (See Japanese page) 
(in English) quantization step / CNN / JPEG compression / histogram / DCT coefficients / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 29, EMM2021-12, pp. 66-71, May 2021.
Paper # EMM2021-12 
Date of Issue 2021-05-13 (IT, EMM) 
ISSN Online edition: ISSN 2432-6380
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IT2021-12 EMM2021-12

Conference Information
Committee EMM IT  
Conference Date 2021-05-20 - 2021-05-21 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Information Security, Information Theory, Information Hiding, etc. 
Paper Information
Registration To EMM 
Conference Code 2021-05-EMM-IT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Feature Extraction Suitable for Double JPEG Compression Analysis Based on Statistical Bias Observation of DCT Coefficients 
Sub Title (in English)  
Keyword(1) quantization step  
Keyword(2) CNN  
Keyword(3) JPEG compression  
Keyword(4) histogram  
Keyword(5) DCT coefficients  
1st Author's Name Daichi Takeshita  
1st Author's Affiliation Okayama University (Okayama Univ.)
2nd Author's Name Minoru Kuribayashi  
2nd Author's Affiliation Okayama University (Okayama Univ.)
3rd Author's Name Nobuo Funabiki  
3rd Author's Affiliation Okayama University (Okayama Univ.)
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Speaker Author-1 
Date Time 2021-05-21 13:35:00 
Presentation Time 25 minutes 
Registration for EMM 
Paper # IT2021-12, EMM2021-12 
Volume (vol) vol.121 
Number (no) no.28(IT), no.29(EMM) 
Page pp.66-71 
Date of Issue 2021-05-13 (IT, EMM) 

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