scholarly journals A Survey of Deep Learning-Based Source Image Forensics

2020 ◽  
Vol 6 (3) ◽  
pp. 9 ◽  
Author(s):  
Pengpeng Yang ◽  
Daniele Baracchi ◽  
Rongrong Ni ◽  
Yao Zhao ◽  
Fabrizio Argenti ◽  
...  
Keyword(s):  
Research Field ◽  
Data Driven ◽  
Source Image ◽  
Excellent Performance ◽  
Important Data ◽  
Image Source ◽  
Source Camera Identification ◽  
Image Forensic ◽  
Camera Identification ◽  
Network Identification

Image source forensics is widely considered as one of the most effective ways to verify in a blind way digital image authenticity and integrity. In the last few years, many researchers have applied data-driven approaches to this task, inspired by the excellent performance obtained by those techniques on computer vision problems. In this survey, we present the most important data-driven algorithms that deal with the problem of image source forensics. To make order in this vast field, we have divided the area in five sub-topics: source camera identification, recaptured image forensic, computer graphics (CG) image forensic, GAN-generated image detection, and source social network identification. Moreover, we have included the works on anti-forensics and counter anti-forensics. For each of these tasks, we have highlighted advantages and limitations of the methods currently proposed in this promising and rich research field.

Wavelet energy feature based source camera identification for ear biometric images

2020 ◽  
Vol 130 ◽  
pp. 139-147 ◽  
Author(s):  
Debbrota Paul Chowdhury ◽  
Sambit Bakshi ◽  
Pankaj Kumar Sa ◽  
Banshidhar Majhi
Keyword(s):  
Wavelet Energy ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Feature Based ◽  
Energy Feature

scholarly journals Source Camera Identification Issues

2011 ◽  
Vol 3 (4) ◽  
pp. 1-15
Author(s):  
Yongjian Hu ◽  
Chang-Tsun Li ◽  
Changhui Zhou ◽  
Xufeng Lin
Keyword(s):  
Classification Accuracy ◽  
Selection Criteria ◽  
Current Source ◽  
Image Features ◽  
Forensic Identification ◽  
Forensic Investigation ◽  
Identification Schemes ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Popular Image

Statistical image features play an important role in forensic identification. Current source camera identification schemes select image features mainly based on classification accuracy and computational efficiency. For forensic investigation purposes; however, these selection criteria are not enough. Consider most real-world photos may have undergone common image processing due to various reasons, source camera classifiers must have the capability to deal with those processed photos. In this work, the authors first build a sample camera classifier using a combination of popular image features, and then reveal its deficiency. Based on the experiments, suggestions for the design of robust camera classifiers are given.

scholarly journals Open-source chemogenomic data-driven algorithms for predicting drug–target interactions

2018 ◽  
Vol 20 (4) ◽  
pp. 1465-1474 ◽  
Author(s):  
Ming Hao ◽  
Stephen H Bryant ◽  
Yanli Wang
Keyword(s):  
Drug Discovery ◽  
Human Genome ◽  
High Throughput Screening ◽  
Drug Target ◽  
Drug Targets ◽  
Drug Repositioning ◽  
Research Field ◽  
Data Driven ◽  
Source Codes ◽  
Novel Technologies

AbstractWhile novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug–target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.

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