Block-Based Convolutional Neural Network for Image Forgery Detection

2017 ◽  
pp. 65-76 ◽  
Author(s):  
Jianghong Zhou ◽  
Jiangqun Ni ◽  
Yuan Rao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Forgery Detection ◽  
Image Forgery ◽  
Image Forgery Detection ◽  
Block Based

Image Forgery Detection Based on Motion Blur Estimated Using Convolutional Neural Network

2019 ◽  
Vol 19 (23) ◽  
pp. 11601-11611 ◽  
Author(s):  
Chunhe Song ◽  
Peng Zeng ◽  
Zhongfeng Wang ◽  
Tong Li ◽  
Lin Qiao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Motion Blur ◽  
Forgery Detection ◽  
Image Forgery ◽  
Image Forgery Detection

Analysis of Image Forgery Detection using Convolutional Neural Network

2021 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Bunil Kumar Balabantaray ◽  
Chiluveru Gnaneshwar ◽  
Satyendra Singh Yadav ◽  
Manish Kumar Singh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Forgery Detection ◽  
Image Forgery ◽  
Image Forgery Detection

Image Forgery: Detection of Manipulated Images Using Neural Network

2020 ◽  
Author(s):  
Nitin Kumar ◽  
Padmesh Naik ◽  
Nikhil Raina ◽  
Deepali Kayande
Keyword(s):  
Neural Network ◽  
Forgery Detection ◽  
Image Forgery ◽  
Image Forgery Detection

scholarly journals An Improved Discrete Cosine Transformation Block Based Scheme for Copy-move Image Forgery Detection

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Abdulraheem Hassanat Oyiza ◽  
Mohd Aizaini Maarof
Keyword(s):  
Detection Algorithm ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Image Block ◽  
Discrete Cosine Transformation ◽  
Image Forgery ◽  
Robust Detection ◽  
Image Forgery Detection ◽  
Dct Coefficients ◽  
Block Based

Copy-moved forgery is a common method to manipulate images. Several attempts of image forgery have been discovered and involves a region been duplicated and copied and pasted on another region of the same image in other to achieve selfish gain. Generally, there are two classification of copy-move forgery detection technique such as the block-based and key point-based. The block-based division is mostly used and divides image into blocks during the stage of image pre-processing before features are extracted, whereas key-point based technique skips the division of image into blocks and directly extracts different local feature from the image. In this paper, we review various block based and key point approach which has been proposed by various researchers. There is a problem of achieving a balance between improving the detection accuracy and having minimal computational complexity. The proposed technique is based on an improved DCT based copy-move image forgery detection (IDB-CFD), which involves using an octagonal block to reduce the number of features for matching, thereby improving detection accuracy while having minimal complexity. The analysis of this work as compared to previous proposed works which is based on a robust detection algorithm for copy-move image forgery (RDA-CF) and involves using circle block to reduce the number of features, results show that previous work represents about 79% of the quantized DCT coefficients on each image block and this proposed work represents about 85% of quantized DCT coefficients, therefore, recovery of about 6% more features using the IDB-CFD technique was observed as the improvement over the previously proposed RDA-CF.

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