Column Level Image Authentication Technique using Hidden Digital Signatures

Khan, Sahib; Wahid, Muneeza; Khan, Tawab; Ahmad, Nasir; Zafar, Mohammad Haseeb

doi:10.23919/iconac.2018.8748967

Cited by 4 publications

(7 citation statements)

References 17 publications

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“…While the method presented in [42] detected image forgery at the column level, the algorithm failed to detect a complete column's or columns' truncation. Moreover, the techniques in [41,42] designated a complete row or column as forged, even if a single pixel was modified in a row or column of an image, respectively. The algorithms located the forged rows or columns instead of forged pixels.…”

Section: Proposed Forgery Detection Techniquesmentioning

confidence: 96%

Section: Proposed Forgery Detection Techniquesmentioning

confidence: 99%

“…The image forgery detection was also addressed in [41,42]. The work in [41] detected image forgery at the row level; however, it failed to detect any modification when a complete row or rows were truncated from an image. While the method presented in [42] detected image forgery at the column level, the algorithm failed to detect a complete column's or columns' truncation.…”

Section: Proposed Forgery Detection Techniquesmentioning

confidence: 99%

“…The work in [41] detected image forgery at the row level; however, it failed to detect any modification when a complete row or rows were truncated from an image. While the method presented in [42] detected image forgery at the column level, the algorithm failed to detect a complete column's or columns' truncation. Moreover, the techniques in [41,42] designated a complete row or column as forged, even if a single pixel was modified in a row or column of an image, respectively.…”

Section: Proposed Forgery Detection Techniquesmentioning

confidence: 99%

“…The limitations present in [41,42] are addressed in the proposed technique. The proposed framework detects image forgery at the pixel level.…”

Section: Proposed Forgery Detection Techniquesmentioning

confidence: 99%

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Forgery Detection and Localization of Modifications at the Pixel Level

Khan

Ali

et al. 2020

Symmetry

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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.

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Section: Proposed Forgery Detection Techniquesmentioning

confidence: 96%

Section: Proposed Forgery Detection Techniquesmentioning

confidence: 99%

Section: Proposed Forgery Detection Techniquesmentioning

confidence: 99%

Section: Proposed Forgery Detection Techniquesmentioning

confidence: 99%

“…The limitations present in [41,42] are addressed in the proposed technique. The proposed framework detects image forgery at the pixel level.…”

Section: Proposed Forgery Detection Techniquesmentioning

confidence: 99%

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Forgery Detection and Localization of Modifications at the Pixel Level

Khan

Ali

et al. 2020

Symmetry

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Digital Steganography and Watermarking for Digital Images: A Review of Current Research Directions

2020

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The development of information technology has led to a significant increase in the share of multimedia traffic in data networks. This has necessitated to solve the following information security tasks in relation to multimedia data: protection against leakage of confidential information, as well as identifying the source of the leak; ensuring the impossibility of unauthorized changes; copyright protection for digital objects. To solve such kind of problems, methods of steganography and watermarking are designed that implement embedding in digital objects hidden information sequences for various purposes. In this paper, an overview of promising research in the specified area is provided. First of all, we provide basic information about this field of research and consider the main applications of its methods. Next, we review works demonstrating current trends in the development of methods and algorithms for data hiding in digital images. This review is not exhaustive; it focuses on contemporary works illustrating current research directions in the field of information embedding in digital images. This is the main feature of review, which distinguishes it from previously published reviews. The paper concludes with an analysis of identified problems in the field of digital steganography and digital watermarking.

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