
Abstract Day to day Digital Image has widely increased popularity in Human life. People edit image with the help of editing tools or software for malicious intent. This work is to identify inconsistency in an image. The paper contains different steps suchas preprocessing, feature extraction, and matching process, which is highlights effective use of local binary pattern method for feature extraction mechanism. Euclidean distance is exploited for matching measures. The result obtained exhibits that LBP with 2x2 block size gives the best result with accuracy reach to ≃ 98.58 % for automatic detection of inconsistencies in an image.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 17 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
