Revealing Traces of Image Resampling and Resampling Antiforensics
Image resampling is a common manipulation in image processing. The forensics of resampling plays an important role in image tampering detection, steganography, and steganalysis. In this paper, we proposed an effective and secure detector, which can simultaneously detect resampling and its forged res...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2017/7130491 |
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| author | Anjie Peng Yadong Wu Xiangui Kang |
| author_facet | Anjie Peng Yadong Wu Xiangui Kang |
| author_sort | Anjie Peng |
| collection | DOAJ |
| description | Image resampling is a common manipulation in image processing. The forensics of resampling plays an important role in image tampering detection, steganography, and steganalysis. In this paper, we proposed an effective and secure detector, which can simultaneously detect resampling and its forged resampling which is attacked by antiforensic schemes. We find that the interpolation operation used in the resampling and forged resampling makes these two kinds of image show different statistical behaviors from the unaltered images, especially in the high frequency domain. To reveal the traces left by the interpolation, we first apply multidirectional high-pass filters on an image and the residual to create multidirectional differences. Then, the difference is fit into an autoregressive (AR) model. Finally, the AR coefficients and normalized histograms of the difference are extracted as the feature. We assemble the feature extracted from each difference image to construct the comprehensive feature and feed it into support vector machines (SVM) to detect resampling and forged resampling. Experiments on a large image database show that the proposed detector is effective and secure. Compared with the state-of-the-art works, the proposed detector achieved significant improvements in the detection of downsampling or resampling under JPEG compression. |
| format | Article |
| id | doaj-art-f839a787f69d41d197499dae5176b166 |
| institution | Kabale University |
| issn | 1687-5680 1687-5699 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-f839a787f69d41d197499dae5176b1662025-02-03T07:24:43ZengWileyAdvances in Multimedia1687-56801687-56992017-01-01201710.1155/2017/71304917130491Revealing Traces of Image Resampling and Resampling AntiforensicsAnjie Peng0Yadong Wu1Xiangui Kang2School of Computer Science and Technology, Southwest University of Science and Technology, Sichuan, ChinaSchool of Computer Science and Technology, Southwest University of Science and Technology, Sichuan, ChinaGuangdong Key Lab of Information Security, School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, ChinaImage resampling is a common manipulation in image processing. The forensics of resampling plays an important role in image tampering detection, steganography, and steganalysis. In this paper, we proposed an effective and secure detector, which can simultaneously detect resampling and its forged resampling which is attacked by antiforensic schemes. We find that the interpolation operation used in the resampling and forged resampling makes these two kinds of image show different statistical behaviors from the unaltered images, especially in the high frequency domain. To reveal the traces left by the interpolation, we first apply multidirectional high-pass filters on an image and the residual to create multidirectional differences. Then, the difference is fit into an autoregressive (AR) model. Finally, the AR coefficients and normalized histograms of the difference are extracted as the feature. We assemble the feature extracted from each difference image to construct the comprehensive feature and feed it into support vector machines (SVM) to detect resampling and forged resampling. Experiments on a large image database show that the proposed detector is effective and secure. Compared with the state-of-the-art works, the proposed detector achieved significant improvements in the detection of downsampling or resampling under JPEG compression.http://dx.doi.org/10.1155/2017/7130491 |
| spellingShingle | Anjie Peng Yadong Wu Xiangui Kang Revealing Traces of Image Resampling and Resampling Antiforensics Advances in Multimedia |
| title | Revealing Traces of Image Resampling and Resampling Antiforensics |
| title_full | Revealing Traces of Image Resampling and Resampling Antiforensics |
| title_fullStr | Revealing Traces of Image Resampling and Resampling Antiforensics |
| title_full_unstemmed | Revealing Traces of Image Resampling and Resampling Antiforensics |
| title_short | Revealing Traces of Image Resampling and Resampling Antiforensics |
| title_sort | revealing traces of image resampling and resampling antiforensics |
| url | http://dx.doi.org/10.1155/2017/7130491 |
| work_keys_str_mv | AT anjiepeng revealingtracesofimageresamplingandresamplingantiforensics AT yadongwu revealingtracesofimageresamplingandresamplingantiforensics AT xianguikang revealingtracesofimageresamplingandresamplingantiforensics |