Digital Watermarking and Steganography

Cox, Ingemar J.; Miller, Matthew; Bloom, Jonathan D.; Fridrich, Jessica; Kalker, Ton

doi:10.1016/b978-0-12-372585-1.x5001-3

Cited by 401 publications

(15 citation statements)

References 306 publications

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“…The modern era begins to embrace data security as an important issue for data science; thus, the researcher concentrates on data hiding for two decades [1]. Data Hiding is a technique by which it can embed secret data into a cover medium where the desired user can extract the embedded data from the marked medium for various purposes [2].…”

Section: Introductionmentioning

confidence: 99%

A Block Mean Insertion and Dual Stego Generation Based Embedding Strategy

Rahman¹,

Kamal²

2021

JCC

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In reversible data hiding, pixel value ordering is an up-to-the-minute research idea in the field of data hiding. Secret messages are embedded in the maximum or the minimum value among the pixels in a block. Pixel value ordering helps identify the embeddable pixels in a block but suffers from fewer embedding payloads. It leaves many pixels in a block without implanting any bits there. The proposed scheme in this paper resolved that problem by allowing every pixel to conceive data bits. The method partitioned the image pixels in blocks of size two. In each block, it first orders these two pixels and then measures the average value. The average value is placed in the middle of these two pixels. Thus, the scheme extends the block size from two to three. After applying the embedding method of Weng et al., the implantation task removed the average value from the block to reduce its size again to two. These two alive pixels are called stego pixels, which produced a stego image. A piece of state information is produced during implanting to track whether a change is happening to the block's cover pixels. This way, after embedding in all blocks, a binary stream of state information is produced, which has later been converted to decimal values. Thus, image data were assembled in a two-dimensional array. Considering the array as another image plane, Weng et al.'s method is again applied to embed further to produce another stego image. Model validation ensured that the proposed method performed better than previous work in this field.

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Section: Introductionmentioning

confidence: 99%

A Block Mean Insertion and Dual Stego Generation Based Embedding Strategy

Rahman¹,

Kamal²

2021

JCC

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“…Для сокрытия факта передачи и самого существования информационного сообщения традиционно используют различные стеганографические техники [1][2][3][4]. С развитием компьютерных наук и цифровых методов обработки информации стеганографическое сокрытие сообщений стало очень распространенным, его используют при обработке изображений, аудио, текстовых документов и пр.…”

Section: Introductionunclassified

“…Для стороннего наблюдателя передаваемые (например, посредством электронной почты) контейнеры, содержащие сокрытые в них информационные сообщения, ничем не отличаются от обычных пользовательских файлов. Это дает возможность организовать скрытый канал связи (КС), не вызывая подозрения о своих намерениях, причем детектировать такие каналы чрезвычайно сложно [1,2].…”

Section: Introductionunclassified

“…Таким образом, полученное изображение-стеганоконтейнер N формируется посредством добавления к исходному изображению I модулированного сообщения (1). Это аналогично тому, как в системах связи передаваемое модулированное информационное сообщение S «складывалось» с естественным фоновым шумом (помехой).…”

Section: Introductionunclassified

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Hiding information in images using pseudo-random sequences

Смірнов¹,

Горбачова²,

Кузнецов³

2020

Computer Science and Cybersecurity

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In this article are discussed techniques of hiding information messages in cover image using direct spectrum spreading technology. This technology is based on the use of poorly correlated pseudorandom (noise) sequences. Modulating the information data with such signals, the message is presented as a noise-like form, which makes it very difficult to detect. Hiding means adding a modulated message to the cover image. If this image is interpreted as noise on the communication channel, then the task of hiding user’s data is equivalent to transmitting a noise-like modulated message on the noise communication channel. At the same it is supposed that noise-like signals are poorly correlated both with each other and with the cover image (or its fragment). However, the latter assumption may not be fulfilled because a realistic image is not an implementation of a random process; its pixels have a strong correlation. Obviously, the selection of pseudo-random spreading signals must take this feature into account. We are investigating various ways of formation spreading sequences while assessing Bit Error Rate (BER) of information data as well as cover image distortion by mean squared error (MSE) and by Peak signal-to-noise ratio (PSNR). The obtained experimental dependencies clearly confirm the advantage of using Walsh sequences. During the research, the lowest BER values were obtained. Even at low values of the signal power of the spreading sequences (P≈5), the BER value, in most cases, did not exceed 0,01. This is the best result of all the sequences under consideration in this work. The values of PSNR when using orthogonal Walsh sequences are, in most cases, comparable to other considered options. However, for a fixed value of PSNR, using the Walsh transform results in significantly lower BER values. It is noted that a promising direction is the use of adaptively generated discrete sequences. So, for example, if the rule for generating expanding signals takes into account the statistical properties of the container, then you can significantly reduce the value of BER. Also, another useful result could be increasing PSNR at a fixed (given) value of BER. The purpose of our work is to justify the choice of extending sequences to reduce BER and MSE (increase PSNR).

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“…The ability of a data hiding technique to remain unchanged to human perception is called imperceptibility i.e. an unintended user should not be able to make out whether the image has undergone watermarking [5]. Robustness is the ability of watermarked data to be immune to attacks and threats.…”

Section: Introductionmentioning

confidence: 99%

A Computational Modelling and Algorithmic Design Approach of Digital Watermarking in Deep Neural Networks

Kavitha¹,

Eranna²,

Giriprasad³

2020

Adv. sci. technol. eng. syst. j.

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In this paper we propose an algorithmic approach for Convolutional Neural Network (CNN) for digital watermarking which outperforms the existing frequency domain techniques in all aspects including security along with the criteria in the neural networks such as conditions embedded, and types of watermarking attack. This research addresses digital watermarking in deep neural networks and with comprehensive experiments through computational modeling and algorithm design, we examine the performance of the built system to demonstrate the potential of watermarking neural networks. The inability of intruder towards the retrieval of data without the knowledge of architecture and keys is also discussed and results of the proposed method are compared with the state of the art methods at different noises and attacks.

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Digital Watermarking and Steganography

Cited by 401 publications

References 306 publications

A Block Mean Insertion and Dual Stego Generation Based Embedding Strategy

A Block Mean Insertion and Dual Stego Generation Based Embedding Strategy

Hiding information in images using pseudo-random sequences

A Computational Modelling and Algorithmic Design Approach of Digital Watermarking in Deep Neural Networks

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