namtpham/image_tampering_detection_references

Image Tampering Detection and Localization

A list of papers, codes and other interesting collections pertaining to image tampering detection and localization.

Classical Filter Based Methods

1. Goh, Jonathan, and Vrizlynn LL Thing. "A hybrid evolutionary algorithm for feature and ensemble selection in image tampering detection." International Journal of Electronic Security and Digital Forensics 7.1 (2015). pdf
   
2. Fan, Jiayuan, Tao Chen, and Jiuwen Cao. "Image tampering detection using noise histogram features." IEEE International Conference on Digital Signal Processing, 2015. pdf
   
3. Gaborini, Lorenzo, et al. "Multi-clue image tampering localization." IEEE International Workshop on Information Forensics and Security, 2014. pdf
4. Muhammad, Ghulam, et al. "Image forgery detection using steerable pyramid transform and local binary pattern." Machine Vision and Applications 25.4, 2014. pdf
   
5. Pun, Chi-Man, Xiao-Chen Yuan, and Xiu-Li Bi. "Image forgery detection using adaptive oversegmentation and feature point matching." IEEE Transactions on Information Forensics and Security, 2015. pdf
   
6. Cozzolino, Davide, Diego Gragnaniello, and Luisa Verdoliva. "Image forgery detection through residual-based local descriptors and block-matching." IEEE International Conference on Image Processing (ICIP), 2014. pdf
   
7. Jaberi, Maryam, et al. "Accurate and robust localization of duplicated region in copy–move image forgery." Machine vision and applications, 2014. pdf
   
8. Lynch, Gavin, Frank Y. Shih, and Hong-Yuan Mark Liao. "An efficient expanding block algorithm for image copy-move forgery detection." Information Sciences, 2013. pdf
   
9. Li, Haodong, et al. "Image Forgery Localization via Integrating Tampering Possibility Maps." IEEE Transactions on Information Forensics and Security, 2017. pdf
   
10. Hashmi, Mohammad Farukh, Vijay Anand, and Avinash G. Keskar. "A copy-move image forgery detection based on speeded up robust feature transform and Wavelet Transforms." International Conference on Computer and Communication Technology (ICCCT), 2014. pdf
    
11. Bianchi, Tiziano, and Alessandro Piva. "Image forgery localization via block-grained analysis of JPEG artifacts." IEEE Transactions on Information Forensics and Security, 2012. pdf
    
12. Nam Thanh Pham, Jong-Weon Lee, Goo-Rak Kwon, and Chun-Su Park. "Efficient image splicing detection algorithm based on markov features." Journal of Multimedia Tools and Applications, vol. 78, no. 9, pp. 12405–12419. 2018.
13. Chen, Can, Scott McCloskey, and Jingyi Yu. "Image Splicing Detection via Camera Response Function Analysis." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017. pdf
    
    

Deep Learning based Methods

1. Chen, Jiansheng, et al. "Median filtering forensics based on convolutional neural networks." IEEE Signal Processing Letters, 2015. pdf
   
2. Bayar, Belhassen, and Matthew C. Stamm. "A deep learning approach to universal image manipulation detection using a new convolutional layer." Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, 2016. pdf
   
3. Liu, Yaqi, et al. "Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks." arXiv preprint arXiv:1706.07842 (2017). pdf
   
4. Rao, Yuan, and Jiangqun Ni. "A deep learning approach to detection of splicing and copy-move forgeries in images." IEEE International Workshop on Information Forensics and Security (WIFS), 2016. pdf
   
5. Bayar, Belhassen, and Matthew C. Stamm. "On the robustness of constrained convolutional neural networks to jpeg post-compression for image resampling detection." IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017. pdf
   
6. Zhou, Jianghong, Jiangqun Ni, and Yuan Rao. "Block-Based Convolutional Neural Network for Image Forgery Detection." International Workshop on Digital Watermarking. Springer, 2017.
   
7. Choi, Hak-Yeol, et al. "Detecting composite image manipulation based on deep neural networks." 2017 International Conference on Systems, Signals and Image Processing (IWSSIP), 2017. pdf
   
8. Bondi, Luca, et al. "Tampering Detection and Localization through Clustering of Camera-Based CNN Features." IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017. pdf
   
9. Bappy, Jawadul H., et al. "Exploiting Spatial Structure for Localizing Manipulated Image Regions." Proceedings of the IEEE International Conference on Computer Vision (ICCV). 2017. pdf
   
10. Bunk, Jason, et al. "Detection and Localization of Image Forgeries using Resampling Features and Deep Learning." IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017. pdf
    
11. Wang, Qing, and Rong Zhang. "Double JPEG compression forensics based on a convolutional neural network." EURASIP Journal on Information Security, 2016. pdf
    
12. Salloum, Ronald, Yuzhuo Ren, and C-C. Jay Kuo. "Image Splicing Localization Using A Multi-Task Fully Convolutional Network (MFCN)." arXiv preprint arXiv:1709.02016 (2017). pdf
    
13. Rota, Paolo, et al. "Bad teacher or unruly student: Can deep learning say something in Image Forensics analysis?." 23rd International Conference on Pattern Recognition (ICPR), 2016. pdf
    
14. Böhme, Rainer, and Matthias Kirchner. "Counter-forensics: Attacking image forensics." Digital Image Forensics. Springer New York, 2013. pdf
    
15. Raghavendra, R., et al. "Transferable Deep-CNN features for detecting digital and print-scanned morphed face images." 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017. pdf