Name
Abstract | ||
|---|---|---|
Tamper detection using image hash is a very common problem of modern days. Several research and advancements have already been done to address this problem. However, most of the existing methods lack the accuracy of tamper detection when the tampered area is low, as well as requiring long image hashes. In this paper, we propose a novel method objectively to minimize the hash length while enhancing the performance at low tampered area. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICAPR.2017.8593100 | 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) |
Keywords | Field | DocType |
image hash,SURF,tamper detection,clustering,k-means | Kernel (linear algebra),Digital watermarking,Pattern recognition,Convolution,Computer science,Feature extraction,Minification,Hash function,Artificial intelligence,Cluster analysis,Detector | Conference |
ISBN | Citations | PageRank |
978-1-5386-2242-1 | 0 | 0.34 |
References | Authors | |
17 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Subhajit Maity | 1 | 0 | 0.68 |
| Ram Kumar Karsh | 2 | 8 | 1.14 |