Boosting CNN-based primary quantization matrix estimation of double JPEG images via a classification-like architecture

Boosting CNN-based primary quantization matrix estimation of double JPEG images via a classification-like architecture

Abstract Estimating the primary quantization matrix of double JPEG compressed images is a problem of relevant importance in image forensics since it allows to infer important information about the past history of an image. In addition, the inconsistencies of the primary quantization matrices across...

Full description

Bibliographic Details
Main Authors: Benedetta Tondi, Andrea Costanzo, Dequ Huang, Bin Li
Format: Article
Language:English
Published: SpringerOpen 2021-05-01
Series:EURASIP Journal on Information Security
Subjects:
Image forensics
Double JPEG compression
Quantization matrix estimation
Deep learning for forensics
Convolutional neural networks
Online Access:https://doi.org/10.1186/s13635-021-00119-0

Internet

https://doi.org/10.1186/s13635-021-00119-0

Similar Items