Detection of Content-Aware Image Resizing Using Seam Properties

2013 ◽  
Vol 284-287 ◽  
pp. 3074-3078 ◽  
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.

scholarly journals Neural network used in Energy Function of Content-aware Image Resizing method to preserve skin color region

2019 ◽  
Author(s):  
Savio Rabelo ◽  
Tamara Pereira ◽  
Nivando Bezerra ◽  
Saulo Oliveira
Keyword(s):  
Neural Network ◽  
Human Skin ◽  
Energy Function ◽  
Skin Color ◽  
Visual Quality ◽  
Superior Performance ◽  
Seam Carving ◽  
Image Resizing ◽  
Original Algorithm ◽  
Content Aware

Seam Carving is a content-aware image resizing method capable of modifying the width or height of pictures. Such an algorithm applies an energy function to evaluate the importance of each pixel in the image. In exceptional cases, such as images that contain people, the method frequently presents deformation of objects due to the energy function not being able to detect a person. In this context, this paper presents a modification of the energy function used in seam carving by employing a neural network which can detect human skin patterns. Such a modification aims at better-preserving people in images. The experiments show that the proposed method achieves superior performance in terms of visual quality through qualitative indexes compared to the original algorithm.

Detection of Content-Aware Image Resizing for Forensic Applications

2014 ◽  
Vol 6 (2) ◽  
pp. 23-39
Author(s):  
Guorui Sheng ◽  
Tiegang Gao ◽  
Shun Zhang
Keyword(s):  
Transition Probability ◽  
Transition Probability Matrix ◽  
Experimental Result ◽  
Seam Carving ◽  
Image Resizing ◽  
Markov Transition ◽  
Digital Image Forgery ◽  
Markov Features ◽  
Content Aware ◽  
Better Than

Seam-Carving is widely used for content-Aware image resizing. To cope with the digital image forgery caused by Seam-Carving, a new detecting algorithm based on Expanded Markov Feature (EMF) is presented. The algorithm takes full advantage of Transition Probability Matrix to represent correlation change caused by Seam-Carving operation. Different with traditional Markov features, the EMF not only reflects the change of correlation within the intra-DCT block, it also represents the change of correlation in more extensive range. The EMF is a fusion of traditional and expanded Markov Transition Probability Matrix. In the proposed algorithm, The EMF of normal image and that of forged image is trained by SVM, and thus the nornal image and forged image by Seam-Carving can be discriminated by SVM. The experimental result shows that the performance of proposed method is better than that of the method based on traditional Markov features and other existing methods

scholarly journals Rate-dependent seam carving and its application to content-aware image coding

2013 ◽  
Vol 2 ◽  
Author(s):  
Yuichi Tanaka ◽  
Taichi Yoshida ◽  
Madoka Hasegawa ◽  
Shigeo Kato ◽  
Masaaki Ikehara
Keyword(s):  
Computational Complexity ◽  
Image Coding ◽  
Side Information ◽  
Image Data ◽  
Seam Carving ◽  
Image Resizing ◽  
Rate Dependent ◽  
Coding Method ◽  
Content Aware ◽  
High Computational Complexity

Content-aware image resizing (CAIR) is desired because it preserves prominent regions in a resized image. However, CAIR requires high computational complexity to perform in mobile devices, though it is desired for these displays. Moreover, transmitting the side information for CAIR from the encoder is a problem since it usually requires high bitrates compared with those for image data. In this paper, we present a rate-dependent CAIR method that produce various retargeting results based on the bitrates for side information. Furthermore, we apply the proposed technique to wavelet-based image coding. Our proposed content-aware image coding method provides good performances for both CAIR and image coding.

Content-aware image resizing: An improved and shadow-preserving seam carving method

2019 ◽  
Vol 155 ◽  
pp. 233-246 ◽  
Author(s):  
Mahdi Hashemzadeh ◽  
Bahareh Asheghi ◽  
Nacer Farajzadeh
Keyword(s):  
Seam Carving ◽  
Image Resizing ◽  
Content Aware

scholarly journals Seam-Carved Image Tampering Detection Based on the Cooccurrence of Adjacent LBPs

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Dengyong Zhang ◽  
Xiao Chen ◽  
Feng Li ◽  
Arun Kumar Sangaiah ◽  
Xiangling Ding
Keyword(s):  
Local Binary Patterns ◽  
Superior Performance ◽  
Detection Accuracy ◽  
Seam Carving ◽  
Feature Classification ◽  
Image Resizing ◽  
Image Tampering ◽  
Texture Information ◽  
Image Forensic ◽  
Image Tampering Detection

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.

Wavelet based seam carving for content-aware image resizing

2009 ◽  
Author(s):  
Jong-Woo Han ◽  
Kang-Sun Choi ◽  
Tae-Shick Wang ◽  
Sung-Hyun Cheon ◽  
Sung-Jea Ko
Keyword(s):  
Seam Carving ◽  
Image Resizing ◽  
Content Aware

Detection of Seam-Carving Image Based on Benford's Law for Forensic Applications

2016 ◽  
Vol 8 (1) ◽  
pp. 51-61 ◽  
Author(s):  
Guorui Sheng ◽  
Tiegang Gao
Keyword(s):  
Experimental Result ◽  
Benford’S Law ◽  
Seam Carving ◽  
Image Resizing ◽  
Benford's Law ◽  
Dct Coefficients ◽  
Digital Image Forgery ◽  
Markov Features ◽  
Content Aware ◽  
Better Than

Seam-Carving is widely used for content-aware image resizing. To cope with the digital image forgery caused by Seam-Carving, a new detecting algorithm based on Benford's law is presented. The algorithm utilize the probabilities of the first digits of quantized DCT coefficients from individual AC modes to detect Seam-Carving images. The experimental result shows that the performance of proposed method is better than that of the method based on traditional Markov features and other existing methods.

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