Countering anti-forensics of lateral chromatic aberration

7Citations
Citations of this article
17Readers
Mendeley users who have this article in their library.

Abstract

Research has shown that lateral chromatic aberrations (LCA), an imaging fingerprint, can be anti-forensically modified to hide evidence of cut-and-paste forgery. In this paper, we propose a new technique for securing digital images against anti-forensic manipulation of LCA. To do this, we exploit resizing differences between color channels, which are induced by LCA anti-forensics, and define a feature vector to quantitatively capture these differences. Furthermore, we propose a detection method that exposes anti-forensically manipulated image patches. The technique algorithm is validated through experimental procedure, showing dependence on forgery patch size as well as anti-forensic scaling factor.

References Powered by Scopus

Pattern Recognition

2005Citations
N/AReaders
Get full text

Exposing digital forgeries by detecting traces of resampling

837Citations
N/AReaders
Get full text

Information forensics: An overview of the first decade

322Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Accurate and Efficient Image Forgery Detection Using Lateral Chromatic Aberration

58Citations
N/AReaders
Get full text

Adversarial multimedia forensics: Overview and challenges ahead

47Citations
N/AReaders
Get full text

Bibliography of digital image anti-forensics and anti-anti-forensics techniques

16Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Mayer, O., & Stamm, M. C. (2017). Countering anti-forensics of lateral chromatic aberration. In IH and MMSec 2017 - Proceedings of the 2017 ACM Workshop on Information Hiding and Multimedia Security (pp. 15–20). Association for Computing Machinery, Inc. https://doi.org/10.1145/3082031.3083242

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 7

64%

Researcher 2

18%

Professor / Associate Prof. 1

9%

Lecturer / Post doc 1

9%

Readers' Discipline

Tooltip

Computer Science 11

92%

Agricultural and Biological Sciences 1

8%

Save time finding and organizing research with Mendeley

Sign up for free