Digital Image Watermarking Using Least Significant Bit Technique in Different Bit Positions

Bansal, Neelam; Deolia, Vinay Kumar; Bansal, Atul; Pathak, Pooja

doi:10.1109/cicn.2014.174

Cited by 9 publications

(5 citation statements)

References 15 publications

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“…Hasil dari penelitian ini yaitu metode LSB terbukti memiliki tingkat keamanan yang tinggi dibanding metode yang lain. Metode steganografi yang lain seperti menyisipkan text ke dalam image dengan menggunakan metode LSB watermarking [6]. Hasil dari penelitian ini yaitu dengan menggunakan algoritma watermarking mampu mempertahankan kualitas citra dari gaussian, poisson, salt dan pepper, serta speckle.…”

Section: Pendahuluanunclassified

Terapan Metode Least Significant Bit untuk Deteksi Keaslian e-Sertifikat

Primawati,

Paramita,

Muchbarak

et al. 2023

FaktorExacta

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The ease of accessing digital information allows someone to change or manipulate the data contained in the information. Therefore, the security of information or important data from intruders or unauthorized access is important. One case that is currently rife is the spread and falsification of information through fake e-certificates. The purpose of this research is to increase security, as well as check the authenticity and validity of data on ecertificates using steganography techniques with the Least Significant Bit (LSB) method. LSB facilitates the insertion of secret messages with fewer pixels of distortion than other steganography methods. However, LSB is more easily detected by steganalysis. The weakness of LSB becomes an advantage to solve the problem of e-certificate authenticity. The image on the certificate in the form of a pdf file will be inserted with information to validate whether the certificate is genuine or not, by reading the pixels in the image in the file. In this research, the data to be inserted is a checksum value form of 32 hexadecimal characters and also an e-certificate information form a JSON string. The result of this research is a web-based application that is able to check the authenticity of e-certificates.

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Section: Pendahuluanunclassified

Terapan Metode Least Significant Bit untuk Deteksi Keaslian e-Sertifikat

Primawati,

Paramita,

Muchbarak

et al. 2023

FaktorExacta

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“…Numerous digital image watermarking methods had been suggested in the literature, spatial domain techniques [1][2][3], frequency domain techniques [4][5][6][7][8][9][10], combination with mathematical models [8][9][10][11][12][13] and recent works combined with machine learning [14][15][16][17][18][19] models. Visible watermarking [1] is the first spatial domain watermarking method that work on the pixel value of the image.…”

Section: Related Workmentioning

confidence: 99%

“…The modifications of pixel intensity values by adding a watermark result in visible or invisible watermarking process. Least significant bit (LSB) modification [2] is the straight forward technique that embeds watermark in least significant bits of image. A digital watermarking method [3] is proposed based on the difference expansion along with LSB.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Based Digital Image Watermarking

2020

jcr

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Watermarking is the method of adding information in digital media for content protection and authentication. Digital image watermarking is one of the solutions to offer value added security on the top of authentication and data encryption for content protection in digital images. The watermarking methods can be frequential and spatial domain methods. In this work, the frequency domain based watermarking method is implemented with the use of discrete wavelet transform (DWT). Along with the frequency domain technique, here we are utilizing the combination of topical developments of the mathematical techniques and most advanced algorithms of machine learning for digital image watermarking process. The mathematical technique considered here is the principal component analysis (PCA) because of its property of dimensionality reduction which further enhances the robustness to watermarking process. The machine learning algorithm taken into account in this work is the support vector machine (SVM) algorithm to increase the accuracy of watermarking process.

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“…The size of cover image is represented as × and the size of logo image is denoted as × /8. The eighth bit of cover image is replaced by each bit of logo image [17]. In this way, the logo image is embedded into the cover image.…”

Section: Least Significant Bitmentioning

confidence: 99%

Image Watermarking Using Two Level Encryption Method Based on Chaotic Logistic Mapping and Rivest Shamir Adleman Algorithm

Venugopal¹,

Reddy²

2018

IJIES

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In this research study, a multipurpose image water-marking approach was developed for delivering selfrecovery, ownership verification and tamper localization of the cover image. For robust watermarking, color and grayscale images were used to deliver wide applicability and generalization to proposed work. Initially, the cover image was converted into wavelet domain by using Integer Wavelet Transform (IWT) and then the logo image was encrypted by using chaotic logistic mapping. This procedure makes the digital image free from false positive error and also helps to provide decent capacity. After encryption and transformation, Least Significant Bit (LSB) was used to provide self-recovery feature as well as to locate the tempered region in the digital image. Then, asymmetric encryption algorithm: Rivest-Shamir-Adleman (RSA) was utilized to improve the hidden data security with high computational efficiency, and good embedding capacity. Finally, the experimental outcome shows that the proposed approach delivers a high secure network with low computational complexity by means of entropy value and Peak Signal-to-Noise Ratio (PSNR). The proposed approach improved PSNR value upto 19 dB and entropy value upto 0.07-0.7 compared to the existing methods: Discrete Cosine Transform (DCT) + Arnold transform and differential evolution algorithm.

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Digital Image Watermarking Using Least Significant Bit Technique in Different Bit Positions

Cited by 9 publications

References 15 publications

Terapan Metode Least Significant Bit untuk Deteksi Keaslian e-Sertifikat

Terapan Metode Least Significant Bit untuk Deteksi Keaslian e-Sertifikat

Machine Learning Based Digital Image Watermarking

Image Watermarking Using Two Level Encryption Method Based on Chaotic Logistic Mapping and Rivest Shamir Adleman Algorithm

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