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1. NTIRE 2024 Restore Any Image Model (RAIM) in the Wild Challenge.

Author

Jie Liang 0007, Radu Timofte, Qiaosi Yi, Shuaizheng Liu, Lingchen Sun, Rongyuan Wu, Xindong Zhang, Hui Zeng, Lei Zhang 0006, Yibin Huang, Shuai Liu 0009, Yongqiang Li, Chaoyu Feng, Xiaotao Wang, Lei Lei, Yuxiang Chen, Xiangyu Chen 0006, Qiubo Chen, Fengyu Sun, Mengying Cui, Jiaxu Chen, Zhenyu Hu, Jingyun Liu, Wenzhuo Ma, Ce Wang, Hanyou Zheng, Wanjie Sun, Zhenzhong Chen, Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao 0004, Jens Sjölund, Thomas B. Schön, Xiong Dun, Pengzhou Ji, Yujie Xing, Xuquan Wang, Zhanshan Wang, Xinbin Cheng, Jun Xiao 0010, Chenhang He, Xiuyuan Wang, Zhi-Song Liu, Zimeng Miao, Zhicun Yin, Ming Liu 0018, Wangmeng Zuo, and Shuai Li 0014

Published
2024
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4. MIPI 2023 Challenge on Nighttime Flare Removal: Methods and Results.

Author

Yuekun Dai, Chongyi Li, Shangchen Zhou, Ruicheng Feng, Qingpeng Zhu, Qianhui Sun, Wenxiu Sun, Chen Change Loy, Jinwei Gu, Shuai Liu 0009, Hao Wang 0073, Chaoyu Feng, Luyang Wang, Guangqi Shao, Chenguang Zhang, Xiaotao Wang, Lei Lei, Dafeng Zhang, Xiangyu Kong, Guanqun Liu 0005, Mengmeng Bai, Jia Ouyang, Xiaobing Wang, Jiahui Yuan, Xinpeng Li, Chengzhi Jiang, Ting Jiang, Wenjie Lin, Qi Wu 0017, Mingyan Han, Jinting Luo, Lei Yu, Haoqiang Fan, Shuaicheng Liu, Bo Yan, Zhuang Li, Yadong Li, Hongbin Wang, Soonyong Song, Minghan Fu, Rayyan Azam Khan, Fang-Xiang Wu, Zhao Zhang 0001, Suiyi Zhao, Huan Zheng, Yangcheng Gao, Yanyan Wei, Jiahuan Ren, Bo Wang 0011, Yan Luo, Shuaibo Gao, Wenhui Wu, Sicong Kang, Nikhil Akalwadi, Ankit Raichur, Vinod Patil, Allabakash Ghodesawar, Swaroop Adrashyappanamath, Amogh Joshi, Chaitra Desai, Ramesh Ashok Tabib, Ujwala Patil, Uma Mudenagudi, Sicheng Li, Ruoxi Zhu, Jiazheng Lian, Shusong Xu, Zihao Liu, Sabari Nathan, and Priya Kansal

Published
2023
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5. NTIRE 2023 Video Colorization Challenge.

Author

Xiaoyang Kang 0002, Xianhui Lin, Kai Zhang 0008, Zheng Hui, Wangmeng Xiang, Jun-Yan He, Xiaoming Li 0002, Peiran Ren, Xuansong Xie, Radu Timofte, Yixin Yang, Jinshan Pan, Zhong Zheng, Peng Qiyan, Jiangxin Zhang, Jinhui Dong, Jinjing Tan, Chi-Chen Lin, Lin Qipei Li, Qirong Liang, Ruipeng Gang, Xiaofeng Liu, Shuang Feng, Shuai Liu 0009, Hao Wang 0073, Chaoyu Feng, Furui Bai, Yuqian Zhang, Guangqi Shao, Xiaotao Wang, Lei Lei, Siqi Chen, Yu Zhang, Hanning Xu, Zheyuan Liu 0009, Zhao Zhang 0001, Yan Luo, and Zhichao Zuo

Published
2023
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7. NTIRE 2023 Challenge on Image Denoising: Methods and Results.

Author

Yawei Li 0001, Yulun Zhang 0001, Radu Timofte, Luc Van Gool, Zhijun Tu, Kunpeng Du, Hailing Wang, Hanting Chen, Wei Li 0002, Xiaofei Wang, Jie Hu 0021, Yunhe Wang 0001, Xiangyu Kong, Jinlong Wu, Dafeng Zhang, Jianxing Zhang, Shuai Liu 0009, Furui Bai, Chaoyu Feng, Hao Wang 0073, Yuqian Zhang, Guangqi Shao, Xiaotao Wang, Lei Lei, Rongjian Xu, Zhilu Zhang, Yunjin Chen, Dongwei Ren, Wangmeng Zuo, Qi Wu 0017, Mingyan Han, Shen Cheng, Haipeng Li, Ting Jiang, Chengzhi Jiang, Xinpeng Li, Jinting Luo, Wenjie Lin, Lei Yu, Haoqiang Fan, Shuaicheng Liu, Aditya Arora, Syed Waqas Zamir, Javier Vazquez-Corral, Konstantinos G. Derpanis, Michael S. Brown, Hao Li 0058, Zhihao Zhao, Jinshan Pan, Jiangxin Dong, Jinhui Tang 0001, Bo Yang 0047, Jingxiang Chen, Chenghua Li, Xi Zhang, Zhao Zhang 0001, Jiahuan Ren, Zhicheng Ji, Kang Miao, Suiyi Zhao, Huan Zheng, Yanyan Wei, Kangliang Liu, Xiangcheng Du, Sijie Liu, Yingbin Zheng, Xingjiao Wu, Cheng Jin 0001, Rajeev Irny, Sriharsha Koundinya, Vighnesh Kamath, Gaurav Khandelwal, Sunder Ali Khowaja, Jiseok Yoon, Ik Hyun Lee, Shijie Chen, Chengqiang Zhao, Huabin Yang, Zhongjian Zhang, Junjia Huang, and Yanru Zhang

Published
2023
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8. NTIRE 2023 Challenge on Night Photography Rendering.

Author

Alina Shutova, Egor I. Ershov, Georgy Perevozchikov, Ivan Ermakov, Nikola Banic, Radu Timofte, Richard Collins, Maria Efimova, Arseniy P. Terekhin, Simone Zini, Claudio Rota, Marco Buzzelli, Simone Bianco 0001, Raimondo Schettini, Chunxia Lei, Tingniao Wang, Song Wang, Shuai Liu 0009, Chaoyu Feng, Guangqi Shao, Hao Wang 0073, Xiaotao Wang, Lei Lei, Lu Xu, Chao Zhang, Yasi Wang, Jin Guo, Yangfan Sun, Tianli Liu, Hao Dejun, Furkan Kinli, Baris özcan, Furkan Kiraç, Hyerin Chung, Nakyung Lee, Sungkeun Kwak, Marcos V. Conde, Tim Seizinger, Florin-Alexandru Vasluianu, Omar Elezabi, Chia-Hsuan Hsieh, Wei-Ting Chen, Hao-Hsiang Yang, Zhi-Kai Huang, Hua-En Chang, I-Hsiang Chen, Yi-Chung Chen, and Yuan-Chun Chiang

Published
2023
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9. NTIRE 2022 Spectral Demosaicing Challenge and Data Set.

Author

Boaz Arad, Radu Timofte, Rony Yahel, Nimrod Morag, Amir Bernat, Yaqi Wu, Xun Wu, Zhihao Fan, Chenjie Xia, Feng Zhang, Shuai Liu 0009, Yongqiang Li, Chaoyu Feng, Lei Lei, Mingwei Zhang, Kai Feng, Xun Zhang, Jiaxin Yao, Yongqiang Zhao 0001, Suina Ma, Fan He, Yangyang Dong, Shufang Yu, Difa Qiu, Jinhui Liu, Mengzhao Bi, Beibei Song, Wenfang Sun, Jiesi Zheng, Bowen Zhao, Yanpeng Cao, Jiangxin Yang, Yanlong Cao, Xiangyu Kong, Jingbo Yu, Yuanyang Xue, and Zheng Xie

Published
2022
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10. NTIRE 2022 Challenge on Night Photography Rendering.

Author

Egor I. Ershov, Alexey Savchik, Denis A. Shepelev, Nikola Banic, Michael S. Brown, Radu Timofte, Karlo Koscevic, Michael Freeman, Vasily Tesalin, Dmitry Bocharov, Illya Semenkov, Marko Subasic, Sven Loncaric, Arseniy P. Terekhin, Shuai Liu 0009, Chaoyu Feng, Hao Wang 0073, Ran Zhu, Yongqiang Li, Lei Lei, Zhihao Li, Si Yi, Linghao Han, Ruiqi Wu, Xin Jin 0005, Chunle Guo, Furkan Kinli, Sami Mentes, Baris özcan, Furkan Kiraç, Simone Zini, Claudio Rota, Marco Buzzelli, Simone Bianco 0001, Raimondo Schettini, Wei Li 0002, Yipeng Ma, Tao Wang, Ruikang Xu, Fenglong Song, Wei-Ting Chen, Hao-Hsiang Yang, Zhi-Kai Huang, Hua-En Chang, Sy-Yen Kuo, Zhexin Liang, Shangchen Zhou, Ruicheng Feng, Chongyi Li, Xiangyu Chen 0006, Binbin Song, Shile Zhang, Lin Liu 0016, Zhendong Wang, Dohoon Ryu, Hyokyoung Bae, Taesung Kwon, Chaitra Desai, Nikhil Akalwadi, Amogh Joshi, Chinmayee Mandi, Sampada Malagi, Akash Uppin, Sai Sudheer Reddy, Ramesh Ashok Tabib, Ujwala Patil, and Uma Mudenagudi

Published
2022
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12. NTIRE 2022 Spectral Recovery Challenge and Data Set.

Author

Boaz Arad, Radu Timofte, Rony Yahel, Nimrod Morag, Amir Bernat, Yuanhao Cai, Jing Lin, Zudi Lin, Haoqian Wang, Yulun Zhang 0001, Hanspeter Pfister, Luc Van Gool, Shuai Liu 0009, Yongqiang Li, Chaoyu Feng, Lei Lei, Jiaojiao Li 0001, Songcheng Du, Chaoxiong Wu, Yihong Leng, Rui Song 0003, Mingwei Zhang, Chongxing Song, Shuyi Zhao, Zhiqiang Lang, Wei Wei 0008, Lei Zhang 0006, Renwei Dian, Tianci Shan, Anjing Guo, Chengguo Feng, Jinyang Liu, Mirko Agarla, Simone Bianco 0001, Marco Buzzelli, Luigi Celona, Raimondo Schettini, Jiang He, Yi Xiao 0003, Jiajun Xiao, Qiangqiang Yuan, Jie Li 0022, Liangpei Zhang 0001, Taesung Kwon, Dohoon Ryu, Hyokyoung Bae, Hao-Hsiang Yang, Hua-En Chang, Zhi-Kai Huang, Wei-Ting Chen, Sy-Yen Kuo, Junyu Chen, Haiwei Li, Song Liu, Sabari Nathan, K. Uma, B. Sathya Bama 0001, and S. Mohamed Mansoor Roomi

Published
2022
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13. Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report.

Author

Marcos V. Conde, Radu Timofte, Yibin Huang, Jingyang Peng, Chang Chen, Cheng Li 0009, Eduardo Pérez-Pellitero, Fenglong Song, Furui Bai, Shuai Liu 0009, Chaoyu Feng, Xiaotao Wang, Lei Lei, Yu Zhu 0004, Chenghua Li, Yingying Jiang 0001, Yong A, Peisong Wang, Cong Leng, Jian Cheng 0001, Xiaoyu Liu 0006, Zhicun Yin, Zhilu Zhang, Junyi Li, Ming Liu 0018, Wangmeng Zuo, Jun Jiang, Jinha Kim, Yue Zhang 0011, Beiji Zou 0002, Zhikai Zong, Xiaoxiao Liu, Juan Marín-Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Furkan Kinli, Baris özcan, Furkan Kiraç, Li Leyi, S. M. Nadim Uddin, Dipon Kumar Ghosh, and Yong Ju Jung

Published
2022
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14. AIM 2022 Challenge on Instagram Filter Removal: Methods and Results.

Author

Furkan Kinli, Sami Mentes, Baris özcan, Furkan Kiraç, Radu Timofte, Yi Zuo, Zitao Wang, Xiaowen Zhang, Yu Zhu 0004, Chenghua Li, Cong Leng, Jian Cheng 0001, Shuai Liu 0009, Chaoyu Feng, Furui Bai, Xiaotao Wang, Lei Lei, Tianzhi Ma, Zi-han Gao, Wenxin He, Woon-Ha Yeo, Wang-Taek Oh, Young-Il Kim, Han-Cheol Ryu, Gang He 0002, Shaoyi Long, S. M. A. Sharif, Rizwan Ali Naqvi, Sungjun Kim, Guisik Kim, Seohyeon Lee, Sabari Nathan, and Priya Kansal

Published
2022
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15. Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report.

Author

Andrey Ignatov, Radu Timofte, Shuai Liu 0009, Chaoyu Feng, Furui Bai, Xiaotao Wang, Lei Lei, Ziyao Yi, Yan Xiang, Zibin Liu, Shaoqing Li, Keming Shi, Dehui Kong, Ke Xu, Minsu Kwon, Yaqi Wu, Jiesi Zheng, Zhihao Fan, Xun Wu, Feng Zhang, Albert No, Minhyeok Cho, Zewen Chen, Xiaze Zhang, Ran Li, Juan Wang, Zhiming Wang, Marcos V. Conde, Ui-Jin Choi, Georgy Perevozchikov, Egor I. Ershov, Zheng Hui, Mengchuan Dong, Xin Lou, Wei Zhou 0037, Cong Pang, Haina Qin, and Mingxuan Cai

Published
2022
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18. Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report

Author

Marcos V. Conde, Radu Timofte, Yibin Huang, Jingyang Peng, Chang Chen, Cheng Li, Eduardo Pérez-Pellitero, Fenglong Song, Furui Bai, Shuai Liu, Chaoyu Feng, Xiaotao Wang, Lei Lei, Yu Zhu, Chenghua Li, Yingying Jiang, Yong A, Peisong Wang, Cong Leng, Jian Cheng, Xiaoyu Liu, Zhicun Yin, Zhilu Zhang, Junyi Li, Ming Liu, Wangmeng Zuo, Jun Jiang, Jinha Kim, Yue Zhang, Beiji Zou, Zhikai Zong, Xiaoxiao Liu, Juan Marín Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Furkan Kınlı, Barış Özcan, Furkan Kıraç, Li Leyi, S. M. Nadim Uddin, Dipon Kumar Ghosh, and Yong Ju Jung

Published
2023

19. Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report

Author

Andrey Ignatov, Radu Timofte, Shuai Liu, Chaoyu Feng, Furui Bai, Xiaotao Wang, Lei Lei, Ziyao Yi, Yan Xiang, Zibin Liu, Shaoqing Li, Keming Shi, Dehui Kong, Ke Xu, Minsu Kwon, Yaqi Wu, Jiesi Zheng, Zhihao Fan, Xun Wu, Feng Zhang, Albert No, Minhyeok Cho, Zewen Chen, Xiaze Zhang, Ran Li, Juan Wang, Zhiming Wang, Marcos V. Conde, Ui-Jin Choi, Georgy Perevozchikov, Egor Ershov, Zheng Hui, Mengchuan Dong, Xin Lou, Wei Zhou, Cong Pang, Haina Qin, and Mingxuan Cai

Published
2023

20. AIM 2022 Challenge on Instagram Filter Removal: Methods and Results

Author

Furkan Kınlı, Sami Menteş, Barış Özcan, Furkan Kıraç, Radu Timofte, Yi Zuo, Zitao Wang, Xiaowen Zhang, Yu Zhu, Chenghua Li, Cong Leng, Jian Cheng, Shuai Liu, Chaoyu Feng, Furui Bai, Xiaotao Wang, Lei Lei, Tianzhi Ma, Zihan Gao, Wenxin He, Woon-Ha Yeo, Wang-Taek Oh, Young-Il Kim, Han-Cheol Ryu, Gang He, Shaoyi Long, S. M. A. Sharif, Rizwan Ali Naqvi, Sungjun Kim, Guisik Kim, Seohyeon Lee, Sabari Nathan, and Priya Kansal

Published
2023

22. NTIRE 2022 Challenge on Night Photography Rendering

Author

Egor Ershov, Alex Savchik, Denis Shepelev, Nikola Banic, Michael S. Brown, Radu Timofte, Karlo Koscevic, Michael Freeman, Vasily Tesalin, Dmitry Bocharov, Illya Semenkov, Marko Subasic, Sven Loncaric, Arseniy Terekhin, Shuai Liu, Chaoyu Feng, Hao Wang, Ran Zhu, Yongqiang Li, Lei Lei, Zhihao Li, Si Yi, Ling-Hao Han, Ruiqi Wu, Xin Jin, Chunle Guo, Furkan Kinli, Sami Mentes, Baris Ozcan, Furkan Kirac, Simone Zini, Claudio Rota, Marco Buzzelli, Simone Bianco, Raimondo Schettini, Wei Li, Yipeng Ma, Tao Wang, Ruikang Xu, Fenglong Song, Wei-Ting Chen, Hao-Hsiang Yang, Zhi-Kai Huang, Hua-En Chang, Sy-Yen Kuo, Zhexin Liang, Shangchen Zhou, Ruicheng Feng, Chongyi Li, Xiangyu Chen, Binbin Song, Shile Zhang, Lin Liu, Zhendong Wang, Dohoon Ryu, Hyokyoung Bae, Taesung Kwon, Chaitra Desai, Nikhil Akalwadi, Amogh Joshi, Chinmayee Mandi, Sampada Malagi, Akash Uppin, Sai Sudheer Reddy, Ramesh Ashok Tabib, Ujwala Patil, Uma Mudenagudi, Ershov, E, Savchik, A, Shepelev, D, Banic, N, Brown, M, Timofte, R, Koscevic, K, Freeman, M, Tesalin, V, Bocharov, D, Semenkov, I, Subasic, M, Loncaric, S, Terekhin, A, Liu, S, Feng, C, Wang, H, Zhu, R, Li, Y, Lei, L, Li, Z, Yi, S, Han, L, Wu, R, Jin, X, Guo, C, Kinli, F, Mentes, S, Ozcan, B, Kirac, F, Zini, S, Rota, C, Buzzelli, M, Bianco, S, Schettini, R, Li, W, Ma, Y, Wang, T, Xu, R, Song, F, Chen, W, Yang, H, Huang, Z, Chang, H, Kuo, S, Liang, Z, Zhou, S, Feng, R, Li, C, Chen, X, Song, B, Zhang, S, Liu, L, Wang, Z, Ryu, D, Bae, H, Kwon, T, Desai, C, Akalwadi, N, Joshi, A, Mandi, C, Malagi, S, Uppin, A, Sudheer Reddy, S, Ashok Tabib, R, Patil, U, and Mudenagudi, U

Subjects
night photography, Photography, Visualization, Computer vision, Conferences, Rendering (computer graphics), Cameras, Pattern recognition
Abstract

This paper reviews the NTIRE 2022 challenge on night photography rendering. The challenge solicited solutions that processed RAW camera images captured in night scenes to produce a photo-finished output image encoded in the standard RGB (sRGB) space. Given the subjective nature of this task, the proposed solutions were evaluated based on the mean opinions of viewers asked to judge the visual appearance of the results. Michael Freeman, a world-renowned photographer, further ranked the solutions with the highest mean opinion scores. A total of 13 teams competed in the final phase of the challenge. The proposed methods provided by the participating teams represent state-of-the-art performance in nighttime photography. Results from the various teams can be found here: https://nightimaging.org/

Published
2022

23. NTIRE 2021 Challenge for Defocus Deblurring Using Dual-pixel Images: Methods and Results

Author

Salman Khan, Ling Shao, Sabari Nathan, Xiaoying Bai, Fahad Shahbaz Khan, Xiaobing Wang, Nisarg Shah, Venkatakrishnan Srinija, Chenghua Li, Tu Vo, Abdullah Abuolaim, Xingyu Hu, Yunan Zhu, Chi Zhang, Zhiwei Xiong, Ruikang Xu, Munawar Hayat, Venkatakrishnan Srivatsav, Zhiwei Zhong, Ruipeng Gang, Chaoyu Feng, Thangavelu Ragavendran, Michael S. Brown, Aditya Arora, Lei Lei, Pengwei Liang, Shuai Liu, Syed Waqas Zamir, Zeyu Xiao, Yiqun Chen, Radu Timofte, Si Miao, Dong Liu, Yiheng Yao, and Dafeng Zhang

Subjects
Signal processing, Deblurring, Atmospheric measurements, Pixel, business.industry, Computer science, Pattern recognition (psychology), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Computer vision, Artificial intelligence, Depth of field, business, Dual (category theory)
Abstract

This paper provides a review of the NTIRE 2021 challenge targeting defocus deblurring using dual-pixel (DP) data. The goal of this single-track challenge was to reduce spatially varying defocus blur present in images captured with a shallow depth of field. The images used in this challenge were obtained using a DP sensor that provided a pair of DP views per captured image. Submitted solutions were evaluated using conventional signal processing metrics, namely peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Out of 185 registered participants, nine teams provided methods and competed in the final stage. The paper describes the methods proposed by the participating teams and their results. The winning teams represent the state-of-the-art in terms of defocus de-blurring using DP images.

Published
2021

24. Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report

Author

Haoqiang Fan, Yibin Huang, Chaoyu Feng, Zhikun Lei, Changbeom Kang, Matteo Maggioni, Donghao Liu, Shen Cheng, Liangyu Chen, Jian Sun, Shuai Xiao, Angeline Pouget, Yifeng Zhou, Myungje Lee, Radu Timofte, Fenglong Song, Tianbao Zhou, Lei Lei, Liguang Huang, Shuaicheng Liu, Feifei Chen, Shuai Liu, Zhongqian Fu, Kim Byeoung-su, Cheng Li, Shinjo Wang, Xiangyu Zhang, Jaeyoon Yoo, Andrey Ignatov, Minsu Kwon, Xin Lu, and Bin Huang

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Chipset, Artificial neural network, Computer science, business.industry, Computer Vision and Pattern Recognition (cs.CV), Deep learning, Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Image processing, Electrical Engineering and Systems Science - Image and Video Processing, Synthetic data, Machine Learning (cs.LG), Pattern recognition (psychology), FOS: Electrical engineering, electronic engineering, information engineering, Computer vision, Artificial intelligence, business, Mobile device, Image resolution
Abstract

Image denoising is one of the most critical problems in mobile photo processing. While many solutions have been proposed for this task, they are usually working with synthetic data and are too computationally expensive to run on mobile devices. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based image denoising solution that can demonstrate high efficiency on smartphone GPUs. For this, the participants were provided with a novel large-scale dataset consisting of noisy-clean image pairs captured in the wild. The runtime of all models was evaluated on the Samsung Exynos 2100 chipset with a powerful Mali GPU capable of accelerating floating-point and quantized neural networks. The proposed solutions are fully compatible with any mobile GPU and are capable of processing 480p resolution images under 40-80 ms while achieving high fidelity results. A detailed description of all models developed in the challenge is provided in this paper., Mobile AI 2021 Workshop and Challenges: https://ai-benchmark.com/workshops/mai/2021/. arXiv admin note: substantial text overlap with arXiv:2105.07809, arXiv:2105.07825

Published
2021

25. Double-compressed JPEG images steganalysis with transferring feature

Author

Xiangwei Kong, Yong Yang, and Chaoyu Feng

Subjects
Steganalysis, Training set, Markov chain, Computer Networks and Communications, Computer science, business.industry, Feature vector, 020207 software engineering, Pattern recognition, 02 engineering and technology, computer.file_format, JPEG, Hardware and Architecture, Feature (computer vision), 0202 electrical engineering, electronic engineering, information engineering, Media Technology, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Software
Abstract

Steganalysis is a technology of detecting the presence of secret messages in digital media. Recently, many algorithms have been proposed and achieved satisfactory detection accuracy. However, the performance of these algorithms will be reduced by double-compression, due to the mismatch between training and testing sets. To address this problem, we proposed Transferring Feature on Double-compressed JPEG images (TFD) to improve the detection accuracy. Specifically, our algorithm consists of two parts. First, we detect the double-compression of testing images by constructing multi-classifier with Markov feature. Then we transfer the steganalysis feature into a new feature space, in order to reduce the difference of feature distributions between training and testing sets. We intend to obtain a transformation matrix by adjusting the expectation and standard deviation of training set, minimizing the feature discrepancy between both sets and keeping classification ability of training set, simultaneously. The experimental results show that the proposed algorithm has better performance in double-compressed mismatched steganalysis.

Published
2018

26. Iterative multi-order feature alignment for JPEG mismatched steganalysis

Author

Yanqing Guo, Xiangwei Kong, Ming Li, and Chaoyu Feng

Subjects
Steganalysis, Steganography, Computer science, business.industry, Cognitive Neuroscience, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 020207 software engineering, Pattern recognition, 02 engineering and technology, computer.file_format, computer.software_genre, JPEG, Computer Science Applications, Set (abstract data type), Transformation (function), Artificial Intelligence, Feature (computer vision), Information hiding, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Data mining, Artificial intelligence, business, computer
Abstract

Modern steganalysis algorithms perform almost perfectly in laboratory conditions where training and testing data are sampled from identical feature distribution. In realistic steganalysis applications, however, the feature distributions of the training set and the testing set are different, which lead to the substantial accuracy degradation of mismatched steganalysis. In this paper, we present an iterative multi-order feature alignment (IMFA) algorithm for JPEG mismatched steganalysis. IMFA tries to transform the training set (source domain) to an intermediate domain, which is close to the first-order and second-order statistics of the testing set (target domain). Then a shared transformation is learnt for intermediate domain and testing set to reduce the higher-order statistics difference of their marginal and conditional distributions, which are measured by Maximum Mean Discrepancy (MMD). Through calibrating the posterior probability of multi-order statistics and repeating iteratively, we can obtain new feature representations which provide enough discrimination for cover and stego images. Experiments on the mismatched JPEG steganalysis under three mismatched conditions are carried out to evaluate our proposed IMFA algorithm. The comparison to prior arts reveals that our proposed IMFA algorithm can significantly improve the accuracy performance in the mismatched conditions.

Published
2016

27. Contribution-based feature transfer for JPEG mismatched steganalysis

Author

Yong Yang, Chaoyu Feng, Ming Li, Yanqing Guo, and Xiangwei Kong

Subjects
Steganalysis, Training set, Computer science, business.industry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 020207 software engineering, Pattern recognition, 02 engineering and technology, computer.file_format, JPEG, Kernel (image processing), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Transform coding
Abstract

In realistic steganalysis applications, the mismatched problem can lead to the degradation of performance in steganalysis. The main reason is the discrepancy of feature distributions between training set and testing set. In this paper, we present a Contribution-based Feature Transfer (CFT) algorithm for JPEG mismatched steganalysis. CFT tries to learn two transformations to transfer training set features by evaluating both the sample feature and dimensional feature contributions. We can obtain new feature representations so as to approach the feature distribution of the testing samples. The comparison to prior arts reveals the superiority of CFT on the experiments for the mismatched JPEG steganalysis in the heterogeneous cover source scenario.

Published
2017

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