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AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI

Kalyani Kadam, Swati Ahirrao, Ketan Kotecha
2021 International Journal of Power Electronics and Drive Systems (IJPEDS)  
This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images.  ...  Experts have applied deep learning techniques to detect a forgery in the image too.  ...  DEEP LEARNING APPROACH FOR COPY MOVE AND IMAGE SPLICING FORGERY DETECTION ConvNets or convolutional neural networks (CNNs) [3] is one of the main categories for image recognition and classifications  ... 
doi:10.11591/ijece.v11i5.pp4489-4501 fatcat:jfxrbki7d5bjpb2i7fhn6e53fm

Detection and Localization of Multiple Image Splicing Using MobileNet V1 [article]

Kalyani Kadam, Dr. Swati Ahirrao, Dr. Ketan Kotecha, Sayan Sahu
2021 arXiv   pre-print
This research work proposes multiple image splicing forgery detection using Mask R-CNN, with a backbone as a MobileNet V1.  ...  Hence there is a need to develop a technique that will detect and locates a multiple image splicing forgery in an image.  ...  Each FCN is trained to deal with image scales of varying sizes. CRF combines the detection findings from these neural networks in an adaptive way.  ... 
arXiv:2108.09674v2 fatcat:ivkqch5a65bqziezqg5i3rohyq

Multi-Supervised Encoder-Decoder for Image Forgery Localization

Chunfang Yu, Jizhe Zhou, Qin Li
2021 Electronics  
Unlike many existing solutions, we employ a semantic segmentation network, named Multi-Supervised Encoder–Decoder (MSED), for the detection and localization of forgery images with arbitrary sizes and multiple  ...  Image manipulation localization is one of the most challenging tasks because it pays more attention to tampering artifacts than to image content, which suggests that richer features need to be learned.  ...  network with multiple supervision modules.  ... 
doi:10.3390/electronics10182255 fatcat:6wwiaphbcnexrh7hjjwrcqy6nq

TransForensics: Image Forgery Localization with Dense Self-Attention [article]

Jing Hao and Zhixin Zhang and Shicai Yang and Di Xie and Shiliang Pu
2021 arXiv   pre-print
The former is to model global context and all pairwise interactions between local patches at different scales, while the latter is used for improving the transparency of the hidden layers and correcting  ...  To tackle this challenging problem, we introduce TransForensics, a novel image forgery localization method inspired by Transformers.  ...  Local noise variances estimation is used for image splicing detection [31] .  ... 
arXiv:2108.03871v1 fatcat:r2bzn53ptbhsbg4d4bo5w2zobq

Fusion of handcrafted and deep features for forgery detection in digital images

Savita Walia, Krishan Kumar, Munish Kumar, Xiao-Zhi Gao
2021 IEEE Access  
In order to use these networks for other image processing tasks such as image forgery detection, deep features extracted from these networks needs to be combined with manually crafted features in order  ...  designed and trained for a different image classification task rather than forgery detection task.  ... 
doi:10.1109/access.2021.3096240 fatcat:momibd4a7vaixobzqzv5bg53sm

A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics

Ivan Castillo Castillo Camacho, Kai Wang
2021 Journal of Imaging  
With this review it can be observed that even if image forgeries are becoming easy to create, there are several options to detect each kind of them.  ...  This paper presents a comprehensive literature review of the image forensics techniques with a special focus on deep-learning-based methods.  ...  [103] proposed a method mainly based on U-Net as a segmentation network for splicing forgery detection.  ... 
doi:10.3390/jimaging7040069 pmid:34460519 pmcid:PMC8321383 fatcat:72zd7nyaifhvlgcxv22zztpm4y

Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis [article]

Arjuna Flenner, Lawrence Peterson, Jason Bunk, Tajuddin Manhar Mohammed, Lakshmanan Nataraj, B.S. Manjunath
2018 arXiv   pre-print
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.  ...  First, resampling features are calculated for image blocks. A deep learning classifier is then used to generate a heatmap that indicates if the image block has been resampled.  ...  The paper is approved for public release, distribution unlimited.  ... 
arXiv:1803.01711v1 fatcat:yjs64koy3jdkzagvzmdxu5ap3u

Non-Semantic Evaluation of Image Forensics Tools: Methodology and Database [article]

Quentin Bammey
2021 arXiv   pre-print
This methodology creates automatically forged images that are challenging to detect for forensic tools and overcomes the problem of creating convincing semantic forgeries.  ...  With the aim of evaluating image forensics tools, we propose a methodology to create forgeries traces, leaving intact the semantics of the image.  ...  Both scenarios pose problems for training neural networks, which risk overfitting on the forgeries' methods and semantic content.  ... 
arXiv:2105.02700v1 fatcat:34woyuwk2veqnaeuggvqyulmta

A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection

Francesco Marra, Diego Gragnaniello, Luisa Verdoliva, Giovanni Poggi
2020 IEEE Access  
Due to limited computational and memory resources, current deep learning models accept only rather small images in input, calling for preliminary image resizing.  ...  In this work, we propose a CNN-based image forgery detection framework which makes decisions based on fullresolution information gathered from the whole image.  ...  INTRODUCTION In this work, we propose a new framework for image forgery detection based on convolutional neural networks (CNN).  ... 
doi:10.1109/access.2020.3009877 fatcat:kvqs5iu7svcklmado3x3z7bony

Detection and Localization of Image Forgeries using Resampling Features and Deep Learning [article]

Jason Bunk, Jawadul H. Bappy, Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Arjuna Flenner, B.S. Manjunath, Shivkumar Chandrasekaran, Amit K. Roy-Chowdhury, Lawrence Peterson
2017 arXiv   pre-print
In the second method, resampling features computed on overlapping image patches are passed through a Long short-term memory (LSTM) based network for classification and localization.  ...  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.  ...  Acknowledgements This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA).  ... 
arXiv:1707.00433v1 fatcat:ztjjc3ompbhjrbnt4xo7l5ktfm

Detection and Localization of Image Forgeries Using Resampling Features and Deep Learning

Jason Bunk, Jawadul H. Bappy, Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Arjuna Flenner, B.S. Manjunath, Shivkumar Chandrasekaran, Amit K. Roy-Chowdhury, Lawrence Peterson
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
In the second method, resampling features computed on overlapping image patches are passed through a Long short-term memory (LSTM) based network for classification and localization.  ...  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.  ...  Figure 3 : 3 Deep Neural networks for detecting resampling in small patches.  ... 
doi:10.1109/cvprw.2017.235 dblp:conf/cvpr/BunkBMNFMCRP17 fatcat:twryuybkanbmvplqiqm7kfmrki

Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis

A. Flenner, L. Peterson, J. Bunk, T.M. Mohammed, L. Nataraj, B.S. Manjunath
2018 IS&T International Symposium on Electronic Imaging Science and Technology  
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.  ...  First, resampling features are calculated for image blocks. A deep learning classifier is then used to generate a heatmap that indicates if the image block has been resampled.  ...  The paper is approved for public release, distribution unlimited.  ... 
doi:10.2352/issn.2470-1173.2018.07.mwsf-212 fatcat:snacz3wjkrgxdefx3tvlojuohu

FOCAL: A Forgery Localization Framework based on Video Coding Self-Consistency [article]

Sebastiano Verde, Paolo Bestagini, Simone Milani, Giancarlo Calvagno, Stefano Tubaro
2020 arXiv   pre-print
The feature extraction step is carried out by means of an explainable convolutional neural network architecture, specifically designed to look for and classify coding artifacts.  ...  The overall framework was validated in two typical forgery scenarios: temporal and spatial splicing.  ...  In [44] the authors propose an autoencoder structure to learn a synthetic model of the source (forgeries are detected as outlier of the learned model) followed by recurrent neural networks (RNNs), implemented  ... 
arXiv:2008.10454v2 fatcat:5zskuuwz5bdk7j7jo3maafk4se

Deep Matching and Validation Network -- An End-to-End Solution to Constrained Image Splicing Localization and Detection [article]

Yue Wu, Wael AbdAlmageed, Prem Natarajan
2017 arXiv   pre-print
We introduce a novel deep convolutional neural network architecture, called Deep Matching and Validation Network (DMVN), which simultaneously localizes and detects image splicing.  ...  Image splicing is a very common image manipulation technique that is sometimes used for malicious purposes.  ...  CONCLUSION In this paper we propose a new deep neural network based solution for the image splicing detection and localization problems. We Figure 10 : Visual comparison of detected masks.  ... 
arXiv:1705.09765v1 fatcat:lrdytkynl5hzlok5q7bp6mej5a

Editorial: Media Authentication and Forensics—New Solutions and Research Opportunities

Edward Delp, Jiwu Huang, Nasir Memon, Anderson Rocha, Matt Turek, Luisa Verdoliva
2020 IEEE Journal on Selected Topics in Signal Processing  
Automated Design of Neural Network Architectures with Reinforcement Learning for Detection of Global Manipulations by Chen et al. addresses the challenge of how to automatically design deep networks for  ...  In this paper, the authors present an order forensics framework for detecting image operator chains based on a convolutional neural network (CNN).  ...  His current research interests focused on multiple areas, including large scale behavior recognition and modeling, object detection and tracking; activity recognition, normalcy modeling and anomaly detection  ... 
doi:10.1109/jstsp.2020.3011085 fatcat:zkjhjzxm3vfathcbk5yyh2o2cy
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