An Overview on Passive Image Forensics Technology for Automatic Computer Forgery

2016 ◽  
Vol 8 (4) ◽  
pp. 14-25 ◽  
Author(s):  
Jie Zhao ◽  
Qiuzi Wang ◽  
Jichang Guo ◽  
Lin Gao ◽  
Fusheng Yang
Keyword(s):  
State Of The Art ◽  
Image Forensics ◽  
Detection Methods ◽  
Image Editing ◽  
Forgery Detection ◽  
Comprehensive Overview ◽  
Automatic Computer ◽  
Photographic Images ◽  
Detection Technology ◽  
Passive Image Forensics

Currently, with the popularity of sophisticated image editing tools like Photoshop, it is becoming very difficult to discriminate between an authentic image and its manipulated version, which poses a serious social problem of debasing the credibility of photographic images as definite records of events. Passive image forgery detection technology, as one main branch of image forensics, has been regarded as the promising research interest due to its versatility and universality. Automatic computer forgery employs computer intelligent algorithms to forge an image in an automatic way, which is rather more complex than copy-move forgery since the source of duplicated region could be non-continuous. In this paper, the authors provide a comprehensive overview of the state-of-the-art passive detection methods for automatic computer forgery.

An Overview on Passive Image Forensics Technology for Automatic Computer Forgery

2020 ◽  
pp. 509-520
Author(s):  
Jie Zhao ◽  
Qiuzi Wang ◽  
Jichang Guo ◽  
Lin Gao ◽  
Fusheng Yang
Keyword(s):  
Image Forensics ◽  
Research Interest ◽  
Detection Methods ◽  
Comprehensive Overview ◽  
Intelligent Algorithms ◽  
Automatic Computer ◽  
Photographic Images ◽  
Passive Detection ◽  
Detection Technology ◽  
Passive Image Forensics

Currently, with the popularity of sophisticated image editing tools like Photoshop, it is becoming very difficult to discriminate between an authentic image and its manipulated version, which poses a serious social problem of debasing the credibility of photographic images as definite records of events. Passive image forgery detection technology, as one main branch of image forensics, has been regarded as the promising research interest due to its versatility and universality. Automatic computer forgery employs computer intelligent algorithms to forge an image in an automatic way, which is rather more complex than copy-move forgery since the source of duplicated region could be non-continuous. In this paper, the authors provide a comprehensive overview of the state-of-the-art passive detection methods for automatic computer forgery.

An Overview on Passive Image Forensics Technology for Automatic Computer Forgery

2020 ◽  
pp. 27-38
Author(s):  
Jie Zhao ◽  
Qiuzi Wang ◽  
Jichang Guo ◽  
Lin Gao ◽  
Fusheng Yang
Keyword(s):  
Image Forensics ◽  
Research Interest ◽  
Detection Methods ◽  
Comprehensive Overview ◽  
Intelligent Algorithms ◽  
Automatic Computer ◽  
Photographic Images ◽  
Passive Detection ◽  
Detection Technology ◽  
Passive Image Forensics

Currently, with the popularity of sophisticated image editing tools like Photoshop, it is becoming very difficult to discriminate between an authentic image and its manipulated version, which poses a serious social problem of debasing the credibility of photographic images as definite records of events. Passive image forgery detection technology, as one main branch of image forensics, has been regarded as the promising research interest due to its versatility and universality. Automatic computer forgery employs computer intelligent algorithms to forge an image in an automatic way, which is rather more complex than copy-move forgery since the source of duplicated region could be non-continuous. In this paper, the authors provide a comprehensive overview of the state-of-the-art passive detection methods for automatic computer forgery.

Passive method for rescale detection using quadrature mirror filter based higher order statistical features

2016 ◽  
Vol 14 (05) ◽  
pp. 1650033 ◽  
Author(s):  
Gajanan K. Birajdar ◽  
Vijay H. Mankar
Keyword(s):  
State Of The Art ◽  
Entropy Measure ◽  
Image Editing ◽  
Neural Network Classifier ◽  
Digital Cameras ◽  
Forgery Detection ◽  
Quadrature Mirror Filter ◽  
Detection Algorithms ◽  
Artificial Neural Network Classifier ◽  
Text Component

High resolution digital cameras and state-of-the-art image editing software tools has given rise to large amount of manipulated images leaving no traces of being subjected to any manipulation. Passive or blind forgery detection algorithms are used in order to determine its authenticity. In this paper, an algorithm is proposed that blindly detects global rescaling operation using the statistical models computed based on quadrature mirror filter (QMF) decomposition. Fuzzy entropy measure is employed to choose the relevant features and to remove non-important features whereas artificial neural network classifier is used for forgery detection. Experimental results are presented on grayscale and [Formula: see text]-component images of UCID database to prove the validity of the algorithm under different interpolation schemes. Results are provided for the detection of rescaled images with JPEG compression, arbitrary cropping and white Gaussian noise addition. Further, results are shown using USC-SIPI database to prove the robustness of the algorithm against the type of database.

scholarly journals Copy-Move Forgery Detection Using Deep Learning

2021 ◽  
Author(s):  
Jawad Khan
Keyword(s):  
State Of The Art ◽  
Image Editing ◽  
Kernel Pca ◽  
Forgery Detection ◽  
Naked Eye ◽  
Rbf Kernel ◽  
Copy Move Forgery Detection ◽  
Interest Point Detector ◽  
Point Detector

Due to the number of image editing tools available online, image tampering has been easy to execute. The quality of these tools has led these tamperings to steer clear from the naked eye. One such tampering method is called the Copy-Move tampering where a region of the image is copied and pasted elsewhere in the image. We propose a method to deal with this. First, the image is broken to blocks using discrete cosine transform. Next, the dimensionality is reduced using the gaussian RBF kernel PCA. Finally, a new iterative interest point detector is proposed and the image is then sent as input to a CNN that predicts whether the image has been forged or not. The experimental results showed that the algorithm gave an excellent percentage of accuracy, outperforming state of the art methods.

scholarly journals An Overview on Image Forensics

2013 ◽  
Vol 2013 ◽  
pp. 1-22 ◽  
Author(s):  
Alessandro Piva
Keyword(s):  
Digital Image ◽  
Prior Information ◽  
State Of The Art ◽  
Image Forensics ◽  
The State ◽  
Comprehensive Overview ◽  
History Of ◽  
Acquisition Device

The aim of this survey is to provide a comprehensive overview of the state of the art in the area of image forensics. These techniques have been designed to identify the source of a digital image or to determine whether the content is authentic or modified, without the knowledge of any prior information about the image under analysis (and thus are defined as passive). All these tools work by detecting the presence, the absence, or the incongruence of some traces intrinsically tied to the digital image by the acquisition device and by any other operation after its creation. The paper has been organized by classifying the tools according to the position in the history of the digital image in which the relative footprint is left: acquisition-based methods, coding-based methods, and editing-based schemes.

scholarly journals MFF-Net: Deepfake Detection Network Based on Multi-Feature Fusion

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1692
Author(s):  
Lei Zhao ◽  
Mingcheng Zhang ◽  
Hongwei Ding ◽  
Xiaohui Cui
Keyword(s):  
Feature Extraction ◽  
Semantic Information ◽  
Feature Fusion ◽  
State Of The Art ◽  
Detection Methods ◽  
Textural Features ◽  
Detection Technology ◽  
Rgb Images ◽  
Signal Processing Methods ◽  
Made In

Significant progress has been made in generating counterfeit images and videos. Forged videos generated by deepfaking have been widely spread and have caused severe societal impacts, which stir up public concern about automatic deepfake detection technology. Recently, many deepfake detection methods based on forged features have been proposed. Among the popular forged features, textural features are widely used. However, most of the current texture-based detection methods extract textures directly from RGB images, ignoring the mature spectral analysis methods. Therefore, this research proposes a deepfake detection network fusing RGB features and textural information extracted by neural networks and signal processing methods, namely, MFF-Net. Specifically, it consists of four key components: (1) a feature extraction module to further extract textural and frequency information using the Gabor convolution and residual attention blocks; (2) a texture enhancement module to zoom into the subtle textural features in shallow layers; (3) an attention module to force the classifier to focus on the forged part; (4) two instances of feature fusion to firstly fuse textural features from the shallow RGB branch and feature extraction module and then to fuse the textural features and semantic information. Moreover, we further introduce a new diversity loss to force the feature extraction module to learn features of different scales and directions. The experimental results show that MFF-Net has excellent generalization and has achieved state-of-the-art performance on various deepfake datasets.

scholarly journals Viruses and Type 1 Diabetes: From Enteroviruses to the Virome

2021 ◽  
Vol 9 (7) ◽  
pp. 1519
Author(s):  
Sonia R. Isaacs ◽  
Dylan B. Foskett ◽  
Anna J. Maxwell ◽  
Emily J. Ward ◽  
Clare L. Faulkner ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Viral Infections ◽  
Antiviral Drug ◽  
Virus Detection ◽  
Detection Methods ◽  
Islet Autoimmunity ◽  
Comprehensive Overview ◽  
Drug Candidates

For over a century, viruses have left a long trail of evidence implicating them as frequent suspects in the development of type 1 diabetes. Through vigorous interrogation of viral infections in individuals with islet autoimmunity and type 1 diabetes using serological and molecular virus detection methods, as well as mechanistic studies of virus-infected human pancreatic β-cells, the prime suspects have been narrowed down to predominantly human enteroviruses. Here, we provide a comprehensive overview of evidence supporting the hypothesised role of enteroviruses in the development of islet autoimmunity and type 1 diabetes. We also discuss concerns over the historical focus and investigation bias toward enteroviruses and summarise current unbiased efforts aimed at characterising the complete population of viruses (the “virome”) contributing early in life to the development of islet autoimmunity and type 1 diabetes. Finally, we review the range of vaccine and antiviral drug candidates currently being evaluated in clinical trials for the prevention and potential treatment of type 1 diabetes.

scholarly journals Macroporous materials: microfluidic fabrication, functionalization and applications

2017 ◽  
Vol 46 (3) ◽  
pp. 855-914 ◽  
Author(s):  
Bingjie Wang ◽  
Pepijn Prinsen ◽  
Huizhi Wang ◽  
Zhishan Bai ◽  
Hualin Wang ◽  
...  
Keyword(s):  
State Of The Art ◽  
The State ◽  
Comprehensive Overview ◽  
Macroporous Materials

This article provides an up-to-date highly comprehensive overview (594 references) on the state of the art of the synthesis and design of macroporous materials using microfluidics and their applications in different fields.

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