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

2020 ◽  
pp. 39-48
Author(s):  
Guorui Sheng ◽  
Tiegang Gao
Keyword(s):  
Digital Image ◽  
Experimental Result ◽  
Benford’S Law ◽  
Seam Carving ◽  
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.

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.

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

Detection of content-aware image resizing based on Benford’s law

2016 ◽  
Vol 21 (19) ◽  
pp. 5693-5701 ◽  
Author(s):  
Guorui Sheng ◽  
Tao Li ◽  
Qingtang Su ◽  
Beijing Chen ◽  
Yi Tang
Keyword(s):  
Benford’S Law ◽  
Image Resizing ◽  
Benford's Law ◽  
Content Aware

HSV Based Image Forgery Detection for Copy-Move Attack

2014 ◽  
Vol 556-562 ◽  
pp. 2825-2828
Author(s):  
Bo Liu ◽  
Chi Man Pun
Keyword(s):  
Digital Image ◽  
Color Space ◽  
Experimental Result ◽  
Forgery Detection ◽  
Image Forgery ◽  
Hsv Color Space ◽  
News Photography ◽  
Image Forgery Detection ◽  
Novel Method ◽  
Digital Image Forgery

As the great development of digital photography and relevant post-processing technology, digital image forgery becomes easily in terms of operating thus may be improperly utilized in news photography in which any forgery is strictly prohibited or the other scenario, for instance, as an evidence in the court. Therefore, digital image forgery detection technique is needed. In this paper, attention has been focused on copy-move forgery that one region is copied and then pasted onto other zones to create duplication or cover something in an image. A novel method based on HSV color space feature is proposed and experimental result will be given and it shows the effectiveness and accurateness of proposed methodology.

Image Seam Carving Based On Content Aware Resizing By Gradient Method

2016 ◽  
pp. 25
Author(s):  
مثيل عماد الدين ◽  
رنا محمد حسن
Keyword(s):  
Gradient Method ◽  
Seam Carving ◽  
Content Aware
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