文件列表:

Feature_set_GDCTF_ACM_2019 (0, 2020-03-14)
Feature_set_GDCTF_ACM_2019\features_GDCTF.m (1305, 2020-03-14)
Feature_set_GDCTF_ACM_2019\ucid00001.tif (590864, 2020-03-14)
Feature_set_κ_IET_2019 (0, 2020-03-14)
Feature_set_κ_IET_2019\demo.m (6210, 2020-03-14)
Feature_set_κ_IET_2019\features_kappa.m (3476, 2020-03-14)
Feature_set_κ_IET_2019\kappacal.m (6752, 2020-03-14)
Feature_set_κ_IET_2019\ucid00001.tif (590864, 2020-03-14)
_config.yml (26, 2020-03-14)
feature_set_SK_ACM2018 (0, 2020-03-14)
feature_set_SK_ACM2018\Cal_o_n_mf_ovrblk_moments.m (9080, 2020-03-14)
feature_set_SK_ACM2018\demo.m (14450, 2020-03-14)
feature_set_SK_ACM2018\features_SK.m (6329, 2020-03-14)
feature_set_SK_ACM2018\ucid00001.tif (590864, 2020-03-14)

FEATURE EXTRACTION CODES FOR MEDIAN FILTERING FORENSICS This repository includes three methods for median filtering forensics in digital images. The codes are created for following publications: 1. A. Gupta and D. Singhal, “Analytical Global Median Filtering Forensics Based on Moment Histograms”, ACM transactions on Multimedia Computing, Communications and Applications (TOMM), vol. 14, no. 2, pp. 44:1–44:23, April 2018. https://doi.org/10.1145/3176650. Feature_set_SK_ACM2018: The folder contains following source codes: a. features_SK.m: function to extract feature set SK for an image (only for testing purposes) b. demo.m: demo to extract training features for original and corresponding median filtered images database c. Cal_o_n_mf_ovrblk_moments.m: function called in script demo.m d. ucid00001.tif: example image from UCID database 2. A. Gupta and D. Singhal, “Global Median Filtering Forensic Method Based on Pearson Parameter Statistics”, IET Image Processing, vol. 13, no. 2, pp. 2045-2057, October 2019. https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6074. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8870583&isnumber=8870575 Feature_set_κ_IET_2019: The folder contains following source codes: a. features_kappa.m: function to extract κ based feature set for an image (only for testing purposes) b. demo.m: demo to extract training features for original and corresponding median filtered images database c. kappacal.m: function called in script demo.m d. ucid00001.tif: example image from UCID database 3. A. Gupta and D. Singhal, “A Simplistic Global Median Filtering Forensics Based on Frequency Domain Analysis of Image Residuals”, ACM Transactions on Multimedia Computing, Communications and Applications, vol. 15, no. 3, pp. 71:1-71:23, August 2019. DOI: https://doi.org/10.1145/3321508 Feature_set_GDCTF_ACM_2019 a. features_GDCTF.m: function to extract GDCTF feature set for an image (for both training and testing purposes) b. ucid00001.tif: example image from UCID database For any queries or to report a bug in the code, please contact Divya Singhal at singhal_dia@yahoo.com HOW TO CITE: If you are using these codes for scholarly or academic research, please cite papers as: 1. A. Gupta and D. Singhal, “Analytical Global Median Filtering Forensics Based on Moment Histograms”, ACM transactions on Multimedia Computing, Communications and Applications (TOMM), vol. 14, no. 2, pp. 44:1–44:23, April 2018. https://doi.org/10.1145/3176650. 2. A. Gupta and D. Singhal, “Global Median Filtering Forensic Method Based on Pearson Parameter Statistics”, IET Image Processing, vol. 13, no. 2, pp. 2045-2057, October 2019. doi: 10.1049/iet-ipr.2018.6074. 3. A. Gupta and D. Singhal, “A Simplistic Global Median Filtering Forensics Based on Frequency Domain Analysis of Image Residuals”, ACM transactions on Multimedia Computing, Communications and Applications (TOMM), vol. 15, no. 3, pp. 71:1-71:23, August 2019.

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