JPEG fragment Carving based on Pixel Similarity of MED_ED
Autor: | Xu Chang, Fanchang Hao, Jian Wu |
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Rok vydání: | 2019 |
Předmět: |
0209 industrial biotechnology
Similarity (geometry) Carving Pixel Physics::Instrumentation and Detectors Image quality Computer science Fragment (computer graphics) business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology computer.file_format JPEG Euclidean distance 020901 industrial engineering & automation Computer Science::Computer Vision and Pattern Recognition 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence Focus (optics) business computer Transform coding |
Zdroj: | 2019 Chinese Control Conference (CCC). |
DOI: | 10.23919/chicc.2019.8865161 |
Popis: | JPEG fragment carving technology is a hot and difficult research field in data recovery and computer forensics. Most of the adjacent Pixel Similarity algorithms for JPEG images have high accuracy when the pixels are continuous and smooth, Low accuracy will occur when image contains sharp areas such as there are strips, vertical strips and angles. SoD and ED only focus on the similarity between adjacent edge pixels, rather than the integrity and smoothness of the whole image. MED uses the surrounding pixel values to predict the target pixel value, which is better than SoD and ED in the prediction of pixel integrity. In order to solve this problem, we provide a new Euclidean distance adjacent pixel detection algorithm MED-ED based on median edge detector, according to the local stability and the characteristics of MED. Secondly, the design algorithm to judge the original cluster size, according to the Features of the file system, in order to solve the problem of inefficiency under the situation of the current storage device capacity soaring and high image quality. The experimental results show that the proposed method can effectively improve the accuracy and efficiency. |
Databáze: | OpenAIRE |
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