Data Hiding in Iris Image for Privacy Protection

Li, Sheng; Chen, Xin; Wang, Zichi; Qian, Zhenxing; Zhang, Xinpeng

doi:10.1080/02564602.2018.1520153

Cited by 15 publications

(6 citation statements)

References 19 publications

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“…The distortion function is designed in two basic steps. First, preliminary entrenching cost is generated for each element of image using clever edge detection technique [20]. The embedding cost is then adjusted using the image's gradient.…”

Section: Methodsmentioning

confidence: 99%

Contemporary Approaches On Reversible Data Hiding In Iris Image Using Deep Learning Techniques

2024

Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security,

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The concept of reversible data concealing is crucial for secret communication. After the extraction stages, the protected information can be completely rebuilt in addition to the hidden data being removed. Pixel, Block and Interpolation based algorithms have seen substantial development over the past decade, employing the spatial-frequency domain and other related techniques. The goal of this research work is to categorize and describe the various methods, inner groups and research papers related to the idea of Reversible Data Hiding (RDH). In order to protect privacy, this research suggests a revolutionary iris image data concealing strategy. Here, private information is incorporated into the iris image in a way that has the least negative effect on iris recognition. The Syndrome Trellis Coding (STC) architecture is applied, limiting changes to the embedded data and to the regions that infrequently affect iris identification. A new distortion function is also proposed to enumerate the impression of data implanting on recognition of Iris, which assigns high embedding cost to locations with strong iris features. According to experimental findings, enough information can be incorporated into the iris image while sustaining excellent identification accuracy by employing the proposed technique.

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Section: Methodsmentioning

confidence: 99%

Contemporary Approaches On Reversible Data Hiding In Iris Image Using Deep Learning Techniques

2024

Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security,

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“…Information integrity, protection, and preservation for multimedia forensic investigation is a vital challenge [26,27]. Figure 2 presents a framework of image-based information hiding for multimedia forensics in an investigational context.…”

Section: Framework Of the Proposed Image-based Information Hidingmentioning

confidence: 99%

IMG‐forensics: Multimedia‐enabled information hiding investigation using convolutional neural network

Khan

Shaikh

Cheikhrouhou

et al. 2021

IET Image Processing

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Information hiding aims to embed a crucial amount of confidential data records into the multimedia, such as text, audio, static and dynamic image, and video. Image-based information hiding has been a significantly important topic for digital forensics. Here, active image deep steganographic approaches have come forward for hiding data. The least significant bit (LSB) steganography approach is proposed to conceal a secret message into the original image. First, the lightweight stream encryption cryptography encrypts secret information in the cover image to protect embedded information from source to destination. Whereas the encrypted embedded cover information into the carrier of stego-image with the help of the LSB and then transmit. In the proposed investigational scheme, a convolutional neural net is used. A model is trained to detect and extract patterns of image hidden features, encrypted stego-image optimization, and classify original and cover images of steganography. Through the experiment result on the forensic image database for mobile steganography of the Center for Statistics and Application in Forensic Evidence, the overall embedded and extracting that the proposed scheme can achieve information hiding as well as revealing with an accuracy rate of 95.1%. The experimental result shows the robustness of the model in terms of efficiency as compared to other state-of-the-art schemes.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Li et al [16], an iris image steganography method, was proposed. This method embeds the personal privacy data in an iris image using ''syndrome trellis coding'' (STC)-based data hiding.…”

Section: Related Studiesmentioning

confidence: 99%

“…Devi et al [18] proposed a method closely related to the method [16], in which some information is embedded in brain MR images, and they checked if the stego-image could still support the same classification accuracy as the original image.…”

Section: Related Studiesmentioning

confidence: 99%

Coverless Image Steganography Based on Optical Mark Recognition and Machine Learning

Mohamed

Hussam

2021

IEEE Access

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The most significant factor to consider during private information transmission through the internet (i.e., insecure channel) is security. So, to keep this data from unauthorized access during transmission, steganography is used. Steganography is the scheme of securing sensitive information by concealing it within carriers such as digital images, videos, audio, text, etc. Current image steganography methods work as follows; it assigns cover image then embeds the secret message within it by pixels' modifications, creating the resultant stego-image. These modifications allow steganalysis algorithms to detect the embedded secret message. So, a coverless data hiding concept is proposed to solve this problem. Coverless does not mean that the secret message will be transmitted without using a cover file, or the cover file can be discarded. Instead, the secret message will be embedded by generating a cover file or a secret message mapping. In this paper, a novel, highly robust coverless image steganography method based on optical mark recognition (OMR) and rule-based machine learning (RBML) is proposed.INDEX TERMS Coverless information hiding, optical mark recognition (OMR), rule-based machine learning (RBML), image steganography.

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Data Hiding in Iris Image for Privacy Protection

Cited by 15 publications

References 19 publications

Contemporary Approaches On Reversible Data Hiding In Iris Image Using Deep Learning Techniques

Contemporary Approaches On Reversible Data Hiding In Iris Image Using Deep Learning Techniques

IMG‐forensics: Multimedia‐enabled information hiding investigation using convolutional neural network

Coverless Image Steganography Based on Optical Mark Recognition and Machine Learning

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