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Detection and Localization of Image Forgeries using Resampling Features and Deep Learning
[article]
2017
arXiv
pre-print
Preserved Fulltext
In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning. ...
We compare the performance of detection/localization of both these methods. Our experimental results show that both techniques are effective in detecting and localizing digital image forgeries. ...
The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. ...
arXiv:1707.00433v1
fatcat:ztjjc3ompbhjrbnt4xo7l5ktfm
Detection and Localization of Image Forgeries Using Resampling Features and Deep Learning
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning. ...
We compare the performance of detection/localization of both these methods. Our experimental results show that both techniques are effective in detecting and localizing digital image forgeries. ...
A deep learning approach to identify facial retouching was proposed in [8] . In [42] , image region forgery detection has been performed using stacked auto-encoder model. ...
doi:10.1109/cvprw.2017.235
dblp:conf/cvpr/BunkBMNFMCRP17
fatcat:twryuybkanbmvplqiqm7kfmrki
Boosting Image Forgery Detection using Resampling Features and Copy-move analysis
[article]
2018
arXiv
pre-print
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We use the copy-move detection method as a pre-filtering step and pass those images that are classified as untampered to a deep learning based resampling detection framework. ...
Realistic image forgeries involve a combination of splicing, resampling, cloning, region removal and other methods. ...
He has been on the organizing and program committees of multiple computer vision and image processing conferences and is serving on the editorial boards of multiple journals.
Author Biography ...
arXiv:1802.03154v2
fatcat:3hfktyx6dbau5bw436d7oykdra
Boosting Image Forgery Detection using Resampling Features and Copy-move Analysis
2018
IS&T International Symposium on Electronic Imaging Science and Technology
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We use the copy-move detection method as a pre-filtering step and pass those images that are classified as untampered to a deep learning based resampling detection framework. ...
Realistic image forgeries involve a combination of splicing, resampling, cloning, region removal and other methods. ...
He has been on the organizing and program committees of multiple computer vision and image processing conferences and is serving on the editorial boards of multiple journals. ...
doi:10.2352/issn.2470-1173.2018.07.mwsf-118
fatcat:7k3uikbtwfemxb4ktpn3byaznu
Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis
[article]
2018
arXiv
pre-print
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In this paper we discuss a method to automatically detect local resampling using deep learning while controlling the false alarm rate using a-contrario analysis. ...
A deep learning classifier is then used to generate a heatmap that indicates if the image block has been resampled. We expect some of these blocks to be falsely identified as resampled. ...
The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. ...
arXiv:1803.01711v1
fatcat:yjs64koy3jdkzagvzmdxu5ap3u
Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis
2018
IS&T International Symposium on Electronic Imaging Science and Technology
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In this paper we discuss a method to automatically detect local resampling using deep learning while controlling the false alarm rate using a-contrario analysis. ...
A deep learning classifier is then used to generate a heatmap that indicates if the image block has been resampled. We expect some of these blocks to be falsely identified as resampled. ...
The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. ...
doi:10.2352/issn.2470-1173.2018.07.mwsf-212
fatcat:snacz3wjkrgxdefx3tvlojuohu
AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI
2021
International Journal of Power Electronics and Drive Systems (IJPEDS)
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Experts have applied deep learning techniques to detect a forgery in the image too. ...
Explainable AI (XAI) algorithms have been used to interpret a black box's decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. ...
Important words are copy move, splicing, deep learning, forgery detection, localization of forgery, segmentation, classification, visualization, explainable, and XAI. ...
doi:10.11591/ijece.v11i5.pp4489-4501
fatcat:jfxrbki7d5bjpb2i7fhn6e53fm
Boundary-based Image Forgery Detection by Fast Shallow CNN
[article]
2018
arXiv
pre-print
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Image forgery detection is the task of detecting and localizing forged parts in tampered images. ...
Previous works mostly focus on high resolution images using traces of resampling features, demosaicing features or sharpness of edges. ...
Many deep learning based methods [2] , [3] , [4] still utilize the resampling features in image forgery detection. ...
arXiv:1801.06732v2
fatcat:fqe7yofj2bdfbp7gtu2ge6pvem
Effect of Error Level Analysis on The Image Forgery Detection Using Deep Learning
2021
Kinetik
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This study wants to know the effect of adding ELA extraction process in the image forgery detection using deep learning approach. ...
The Convolutional Neural Network (CNN), which is a deep learning method, is used as a method to do the image forgery detection. ...
Hopefully, this research can make a major contribution to the advancement of technology in Indonesia. ...
doi:10.22219/kinetik.v6i3.1272
fatcat:abelkvfe5zhrrh2oh7en5kckrq
Semantic Modeling and Pixel Discrimination for Image Manipulation Detection
2022
Security and Communication Networks
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Specifically, the pixel-level detection branch resamples features and uses an LSTM to detect manipulations, such as resampling, rotation, and cropping. ...
In this paper, the detection of image manipulation areas based on forgery object detection and pixel discrimination is proposed. ...
Acknowledgments is work was supported by the Basic Scientific Research Operating Expenses of the Academy of Broadcasting Science, NRTA (No. JBKY2020006). ...
doi:10.1155/2022/9755509
fatcat:igf4spljqng7zbbfkffgfhi2c4
Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries
[article]
2019
arXiv
pre-print
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Resampling features are used to capture artifacts like JPEG quality loss, upsampling, downsampling, rotation, and shearing. ...
Finally, decoder network learns the mapping from low-resolution feature maps to pixel-wise predictions for image tamper localization. ...
A deep learning approach to identify facial retouching was proposed in [11] . In [88] , image region forgery detection has been performed using stacked auto-encoder model. ...
arXiv:1903.02495v1
fatcat:bu6iacqbhvbbxboebyfwwu2qne
Hybrid Algorithm for the detection of Pixel-based digital image forgery using Markov and SIFT descriptors
2021
Revista Facultad de Ingeniería
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This investigation introduces an algorithm to detect the main types of pixel-based alterations such as copy-move forgery, resampling, and splicing in digital images. ...
Of 7100 images evaluated, 3666 were unaltered, 791 had resampling, 2213 had splicing, and 430 had copy-move forgeries. ...
As copy-move detection, a deep learning approach is used for splicing detection. ...
doi:10.17533/udea.redin.20211165
fatcat:zzyz3alykfd6hmhjzif6wxrlry
Efficient resampling features and convolution neural network model for image forgery detection
2022
Indonesian Journal of Electrical Engineering and Computer Science
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This paper provides a competent tampering detection technique using resampling features and convolution neural network (CNN). ...
The image forensic approach has been employed for detecting whether or not an image has been manipulated with the usage of positive attacks which includes splicing, and copy-move. ...
EFFICIENT RSF AND CNN MODEL FOR IMAGE FORGERY DETECTION Here the tampering detection through resampling feature extraction and CNN descriptor is presented. ...
doi:10.11591/ijeecs.v25.i1.pp183-190
fatcat:bqbmlxb6fvejxnu2j4gtfke75e
Copy Move Forgery Detection Techniques: A Comprehensive Survey of Challenges and Future Directions
2021
International Journal of Advanced Computer Science and Applications
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Digital Image Forensics is a growing field of image processing that attempts to gain objective proof of the origin and veracity of a visual image. ...
In this survey, we cover the conventional and the deep learning based CMFD techniques from a new perspective. ...
Object detection networks such as R-CNN, and Faster R-CNN are able to localize the forgery using bounding boxes. ...
doi:10.14569/ijacsa.2021.0120729
fatcat:5kebiko6ojg3ppcp7htl5nnrrq
Universal Image Manipulation Detection using Deep Siamese Convolutional Neural Network
[article]
2018
arXiv
pre-print
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It gives the information about the processing history of an image, and also can expose forgeries present in an image. ...
To solve this problem, we propose a novel deep learning-based method which can differentiate between different types of image editing operations. ...
This is the first deep learning-based image manipulation detection method, where the first layer computes the median filtering residual and the subsequent layers extract and classify the features useful ...
arXiv:1808.06323v2
fatcat:avr3wjzdmbdwngx2n4eio6h2xq
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