Abstract

In this paper, we present a new technique of image forgery detection. The proposed technique uses digital signatures embedded in the least significant bits of the selected pixels of each row and column. The process maintains a symmetry in the use of pixels for computing and hiding the digital signatures. Each row and column of the image symmetrically contributes to both processes, with the number of pixels per row or column used for computing the signature, and the pixels used for embedding are not equal and are asymmetric. The pixels in each row and column of an image are divided into two groups. One group contains pixels of a row or column used in the calculation of digital signatures, and the second group of pixels is used for embedding the digital signatures of the respective row or column. The digital signatures are computed using the hash algorithm, e.g., message digest five (MD5). The least significant bits substitution technique is used for embedding the computed digital signature in the least significant bits of the selected pixels of the corresponding row or column. The proposed technique can successfully detect the modification made in an image. The technique detects pixel level modification in a single or multiple pixels.

Highlights

  • With the growth of technology, high resolution digital cameras are available at reasonable prices

  • Digital cameras are available on each smartphone

  • The digital signatures are embedded in the LSBs of the selected pixels of the corresponding rows and columns

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Summary

Introduction

With the growth of technology, high resolution digital cameras are available at reasonable prices. Images are modified with the help of advanced tools in such a manner that the modifications are imperceptible to the HVS, and it is difficult, and almost impossible, to detect the manipulation with the naked eye [2]. Such variations give birth to severe vulnerabilities and risk the integrity of the digital images. Trusted and authentic images are presented in a court of law, especially when the digital contents are produced as legal evidence in front of judges [3]. Passive techniques use the statistics of images for forgery detection and content authentication.

Proposed Forgery Detection Techniques
Experimental Results and Analysis
Comparison
Conclusions

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