New Image Steganography Method Based on Haar Discrete Wavelet Transform

Taouil, Youssef; Ameur, El Bachir; Belghiti, Moulay Taïb

doi:10.1007/978-3-319-46568-5_30

Cited by 10 publications

(8 citation statements)

References 6 publications

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“…To avoid the loss of data from the floating point, the embedding is done in the integer part of the transform coefficients. This is done in such a way that it increases both, the imperceptibility and the capacity of hiding [3].…”

Section: Hwtmentioning

confidence: 99%

Biomedical Image Augmentation and Transmission Using Steganography

Satapathy

Gokhale²

2022

Asian J Pharm Clin Res

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Objective: The aim of the study was to embed sensitive patient information and image data in a cover image to enable secure transmission of confidential data. Methods: Image steganography is a process used to embed (hide with encryption) data which includes text, images or audio, and video files inside the main image file. This embedding is done by altering the values of some pixels which are chosen by the encryption algorithm. The algorithm used in this case is the Discrete Wavelet Transformation Algorithm using MATLAB. Results: Statistical features such as signal to noise ratio (SNR), Peak SNR, and (mean squared error) were extracted from the medical images to test the loss during steganography and transmission. Conclusion: A technique that enables secure and swift image data transmission using image steganography technique is proposed.

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Section: Hwtmentioning

confidence: 99%

Biomedical Image Augmentation and Transmission Using Steganography

Satapathy

Gokhale²

2022

Asian J Pharm Clin Res

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“…Taouil Y. et al, developing the image steganography process by applying the Haar discrete wavelet transform [27]. This process is used to hide the data in the frequency domain because of the robust area.…”

Section: Related Workmentioning

confidence: 99%

Adopt an optimal location using a genetic algorithm for audio steganography

Raheema¹

2021

PEN

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With the development of technologies, most of the users utilizing the Internet for transmitting information from one place to another place. The transmitted data may be affected because of the intermediate user. Therefore, the steganography approach is applied for managing the secret information. Here audio steganography is utilized to maintain the secret information by hiding the image into the audio files. In this work, discrete cosine transforms, and discrete wavelet transform is applied to perform the Steganalysis process. The optimal hiding location has been identified by using the optimization technique called a genetic algorithm. The method utilizes the selection, crossover and mutation operators for selecting the best location. The chosen locations are difficult to predict by unauthorized users because the embedded location is varied from information to information. Then the efficiency of the system ensures the high PSNR, structural similarity index (SSIM), minimum mean square error value and Jaccard, which is evaluated on the audio Steganalysis dataset.

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“…Unlike cryptography, which encrypts transmitted secure data, steganography simply hides the data from a third party. Images are the most widely used medium in steganography; an image is easy to handle; and considering images are pretty common on the Internet, they do not arise suspicion [2]. Concealing information into a carrier (cover) image produces a new image (stego image); this latter image carries the information that is secretly transferred without the awareness of a third-party.…”

Section: Introductionmentioning

confidence: 99%

CNN Auto-Encoder Network Using Dilated Inception for Image Steganography

Kich¹,

Ameur²,

Taouil³

2021

IJFIS

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Numerous studies have used convolutional neural networks (CNNs) in the field of information concealment as well as steganalysis, achieving promising results in terms of capacity and invisibility. In this study, we propose a CNN-based steganographic model to hide a color image within another color image. The proposed model consists of two sub-networks: the hiding network is used by the sender to conceal the secret image; and the reveal network is used by the recipient to extract the secret image from the stego image. The architecture of the concealment sub-network is inspired by the U-Net auto-encoder and benefits from the advantages of the dilated convolution. The reveal sub-network is inspired by the auto-encoder architecture. To ensure the integrity of the hidden secret image, the model is trained end to end: rather than training separately, the two sub-networks are trained simultaneously a pair of networks. The loss function is elaborated in such a way that it favors the quality of the stego image over the secret image as the stego image is the one that comes under steganalysis attacks. To validate the proposed model, we carried out several tests on a range of challenging publicly available image datasets such as ImageNet, Labeled Faces in the Wild (LFW), and PASCAL-VOC12. Our results show that the proposed method can dissimulate an image into another one with the same size, reaching an embedding capacity of 24 bit per pixel without generating visual or structural artefacts on the host image. In addition, the proposed model is generic, that is, it does not depend on the image's size or the database source.

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New Image Steganography Method Based on Haar Discrete Wavelet Transform

Cited by 10 publications

References 6 publications

Biomedical Image Augmentation and Transmission Using Steganography

Biomedical Image Augmentation and Transmission Using Steganography

Adopt an optimal location using a genetic algorithm for audio steganography

CNN Auto-Encoder Network Using Dilated Inception for Image Steganography

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