Detecting seam carving based image resizing using local binary patterns

Seam carving has been widely used in image resizing due to its superior performance in avoiding image distortion and deformation, which can maliciously be used on purpose, such as tampering contents of an image. As a result, seam-carving detection is becoming crucially important to recognize the image authenticity. However, existing methods do not perform well in the accuracy of seam-carving detection especially when the scaling ratio is low. In this paper, we propose an image forensic approach based on the cooccurrence of adjacent local binary patterns (LBPs), which employs LBP to better display texture information. Specifically, a total of 24 energy-based, seam-based, half-seam-based, and noise-based features in the LBP domain are applied to the seam-carving detection. Moreover, the cooccurrence features of adjacent LBPs are combined to highlight the local relationship between LBPs. Besides, SVM after training is adopted for feature classification to determine whether an image is seam-carved or not. Experimental results demonstrate the effectiveness in improving the detection accuracy with respect to different scaling ratios, especially under low scaling ratios.

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Improved Image Resizing using Seam Carving and scaling

Proceedings of 3rd International Conference on Multimedia Technology(ICMT-13) ◽

10.2991/icmt-13.2013.102 ◽

2013 ◽

Author(s):

Zhang Yan ◽

Sun Jonathan Z. ◽

Peng Jingliang

Keyword(s):

Seam Carving ◽

Image Resizing

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Detection of Tampering by Image Resizing Using Local Tchebichef Moments

Applied Sciences ◽

10.3390/app9153007 ◽

2019 ◽

Vol 9 (15) ◽

pp. 3007

Author(s):

Dengyong Zhang ◽

Shanshan Wang ◽

Jin Wang ◽

Arun Kumar Sangaiah ◽

Feng Li ◽

...

Keyword(s):

Texture Classification ◽

Binary Classification ◽

Classification Problem ◽

Seam Carving ◽

Spatial Correlations ◽

Image Resizing ◽

Tchebichef Moments ◽

Output Code ◽

Universal Detection ◽

Multi Class Classification

There are many image resizing techniques, which include scaling, scale-and-stretch, seam carving, and so on. They have their own advantages and are suitable for different application scenarios. Therefore, a universal detection of tampering by image resizing is more practical. By preliminary experiments, we found that no matter which image resizing technique is adopted, it will destroy local texture and spatial correlations among adjacent pixels to some extent. Due to the excellent performance of local Tchebichef moments (LTM) in texture classification, we are motivated to present a detection method of tampering by image resizing using LTM in this paper. The tampered images are obtained by removing the pixels from original images using image resizing (scaling, scale-and-stretch and seam carving). Firstly, the residual is obtained by image pre-processing. Then, the histogram features of LTM are extracted from the residual. Finally, an error-correcting output code strategy is adopted by ensemble learning, which turns a multi-class classification problem into binary classification sub-problems. Experimental results show that the proposed approach can obtain an acceptable detection accuracies for the three content-aware image re-targeting techniques.

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Seam carving based image resizing detection using hybrid features

Tehnicki vjesnik - Technical Gazette ◽

10.17559/tv-20160804121351 ◽

2017 ◽

Vol 24 (6) ◽

Keyword(s):

Seam Carving ◽

Image Resizing ◽

Hybrid Features

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Accumulative energy-based seam carving for image resizing

International Journal of Computational Science and Engineering ◽

10.1504/ijcse.2018.10014036 ◽

2018 ◽

Vol 1 (1) ◽

pp. 1

Author(s):

Haifeng Zhang ◽

Yuzhen Niu ◽

Yuqing Lin ◽

Jiawen Lin

Keyword(s):

Seam Carving ◽

Image Resizing

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Detection of Content-Aware Image Resizing Using Seam Properties

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.284-287.3074 ◽

2013 ◽

Vol 284-287 ◽

pp. 3074-3078 ◽

Cited By ~ 3

Author(s):

Seung Jin Ryu ◽

Hae Yeoun Lee ◽

Heung Kyu Lee

Keyword(s):

Empirical Evidence ◽

Superior Performance ◽

Seam Carving ◽

Large Database ◽

Image Resizing ◽

Image Content ◽

Content Aware

Visually convincing content-aware image resizing, which preserves semantically important image content, has been actively researched in recent years. This paper proposes a resizing detector that reveals the trace of seam carving and seam insertion. To unveil the evidence of seam carving, we exploit energy bias of seam carved images. In addition, the correlation between adjacent pixels is analyzed to estimate the inserted seams. Empirical evidence from a large database of test images demonstrates the superior performance of the proposed detector under a variety of settings.

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