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A Survey of Deep Learning-Based Source Image Forensics.

Pengpeng Yang^1,2, Daniele Baracchi³, Rongrong Ni^1,2

¹Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China.

Journal of Imaging

|August 30, 2021

Summary

This summary is machine-generated.

This survey explores data-driven methods for digital image forensics, covering source camera identification, recaptured images, computer graphics, GANs, and social networks. It analyzes algorithms for verifying image authenticity and integrity.

Keywords:

data driven methods image forensics multimedia forensics source identification

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Area of Science:

Computer Vision
Digital Forensics
Machine Learning

Background:

Digital image forensics verifies authenticity and integrity.
Data-driven approaches show promise, inspired by computer vision successes.
Existing research lacks a structured overview of data-driven methods.

Purpose of the Study:

To survey and categorize key data-driven algorithms in image source forensics.
To provide a structured overview of the field, aiding researchers.
To highlight the advantages and limitations of current forensic techniques.

Main Methods:

Systematic review of data-driven algorithms for image forensics.
Categorization of methods into five sub-topics: source camera identification, recaptured image forensic, computer graphics (CG) image forensic, GAN-generated image detection, and source social network identification.
Inclusion of research on anti-forensics and counter anti-forensics.

Main Results:

Identified and categorized major data-driven algorithms across five key areas of image forensics.
Detailed the strengths and weaknesses of various forensic techniques.
Provided a comprehensive overview of the current landscape in data-driven image forensics.

Conclusions:

Data-driven methods are crucial for blind verification of digital image authenticity.
The field is rapidly evolving with diverse applications and ongoing challenges in anti-forensics.
Further research is needed to address limitations and advance the robustness of forensic algorithms.