A convolutional neural network based seam carving detection scheme for uncompressed digital images

Document Type

Conference Proceeding

Publication Date

1-1-2019

Abstract

Revealing the processing history that a given digital image has gone through is an important topic in digital image forensics. Detection of seam carving, a content-aware image scaling algorithm commonly implemented in commercial image-editing software, has been studied by forensic experts in recent years. In this paper, a convolutional neural network (CNN) architecture is proposed for seam carving detection. Unlike the existing forensic works in detecting seam carving, where the feature selection and the pattern classification are two separated procedures, the proposed CNN-based deep learning architecture learns and then uses more effective features via joint optimization of feature extraction and pattern classification. Experimental results conducted on a large dataset have demonstrated that, compared with the current state-of-the-art, the proposed CNN based deep learning scheme can largely boost the classification rates as the seam carving rate is rather low.

Identifier

85061365548 (Scopus)

ISBN

[9783030113889]

Publication Title

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

External Full Text Location

https://doi.org/10.1007/978-3-030-11389-6_1

e-ISSN

16113349

ISSN

03029743

First Page

3

Last Page

13

Volume

11378 LNCS

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